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Journal of Machine Learning Technologies

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011, pp-37-63

Available online at http://www.bioinfo.in/contents.php?id=51

Copyright © 2011 Bioinfo Publications 37

EVALUATION: FROM PRECISION, RECALL AND F-MEASURE TO ROC,

INFORMEDNESS, MARKEDNESS & CORRELATION

POWERS, D.M.W.

*AILab, School of Computer Science, Engineering and Mathematics, Flinders University, South Australia, Australia

Corresponding author. Email: David.Powers@flinders.edu.au

Received: February 18, 2011; Accepted: February 27, 2011

Abstract-Commonly used evaluation measures including Recall, Precision, F-Measure and Rand Accuracy are

biased and should not be used without clear understanding of the biases, and corresponding identification of chance

or base case levels of the statistic. Using these measures a system that performs worse in the objective sense of

Informedness, can appear to perform better under any of these commonly used measures. We discuss several

concepts and measures that reflect the probability that prediction is informed versus chance. Informedness and

introduce Markedness as a dual measure for the probability that prediction is marked versus chance. Finally we

demonstrate elegant connections between the concepts of Informedness, Markedness, Correlation and Significance

as well as their intuitive relationships with Recall and Precision, and outline the extension from the dichotomous case

to the general multi-class case.

Keywords–Recall and Precision, F-Measure, Rand Accuracy, Kappa,Informedness and Markedness, DeltaP,

Correlation, Significance.

INTRODUCTION

A common but poorly motivated way of evaluating

results of Machine Learning experiments is using

Recall, Precision and F-measure. These measures

are named for their origin in Information Retrieval and

present specific biases, namely that they ignore

performance in correctly handling negative examples,

they propagate the underlying marginal prevalences

and biases, and they fail to take account the chance

level performance. In the Medical Sciences, Receiver

Operating Characteristics (ROC) analysis has been

borrowed from Signal Processing to become a

standard for evaluation and standard setting,

comparing True Positive Rate and False Positive

Rate. In the Behavioural Sciences, Specificity and

Sensitivity, are commonly used. Alternate techniques,

such as Rand Accuracy and Cohen Kappa, have

some advantages but are nonetheless still biased

measures. We will recapitulate some of the literature

relating to the problems with these measures, as well

as considering a number of other techniques that

have been introduced and argued within each of

these fields, aiming/claiming to address the problems

with these simplistic measures.

This paper recapitulates and re-examines the

relationships between these various measures,

develops new insights into the problem of measuring

the effectiveness of an empirical decision system or a

scientific experiment, analyzing and introducing new

probabilistic and information theoretic measures that

overcome the problems with Recall, Precision and

their derivatives.

THE BINARY CASE

It is common to introduce the various measures in the

context of a dichotomous binary classification

problem, where the labels are by convention + and-

and the predictions of a classifier are summarized in

a four-cell contingency table. This contingency table

may be expressed using raw counts of the number of

times each predicted label is associated with each

real class, or may be expressed in relative terms.

Cell and margin labels may be formal probability

expressions, may derive cell expressions from margin

labels or vice-versa, may use alphabetic constant

labels a, b, c, d or A, B, C, D, or may

use acronyms for the generic terms for True and

False, Real and Predicted Positives and Negatives.

Often UPPER CASE is used where the values are

counts, and lower case letters where the values are

probabilities or proportions relative to N or the

marginal probabilities – we will adopt this convention

throughout this paper (always written in

typewriter font), and in addition will use

Mixed Case (in the normal text font) for popular

nomenclature that may or may not correspond

directly to one of our formal systematic names. True

and False Positives (TP/FP) refer to the number of

Predicted Positives that were correct/incorrect, and

similarly for True and False Negatives (TN/FN), and

these four cells sum to N. On the other hand tp,

fp, fn, tn and rp, rn and pp, pn refer

to the joint and marginal probabilities, and the four

contingency cells and the two pairs of marginal

probabilities each sum to 1. We will attach other

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

38 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

popular names to some of these probabilities in due

course.

We thus make the specific assumptions that we are

predicting and assessing a single condition that is

either positive or negative (dichotomous), that we

have one predicting model, and one gold standard

labeling. Unless otherwise noted we will also for

simplicity assume that the contingency is non-trivial in

the sense that both positive and negative states of

both predicted and real conditions occur, so that none

of the marginal sums or probabilities is zero.

We illustrate in Table 1 the general form of a binary

contingency table using both the traditional alphabetic

notation and the directly interpretable systematic

approach. Both definitions and derivations in this

paper are made relative to these labellings, although

English terms (e.g. from Information Retrieval) will

also be introduced for various ratios and probabilities.

The green positive diagonal represents correct

predictions, and the pink negative diagonal incorrect

predictions. The predictions of the contingency table

may be the predictions of a theory, of some

computational rule or system (e.g. an Expert System

or a Neural Network), or may simply be a direct

measurement, a calculated metric, or a latent

condition, symptom or marker. We will refer

generically to "the model" as the source of the

predicted labels, and "the population" or "the world"

as the source of the real conditions. We are

interested in understanding to what extent the model

"informs" predictions about the world/population, and

the world/population "marks" conditions in the model.

Recall & Precision, Sensitivity & Specificity

Recall or Sensitivity (as it is called in Psychology) is

the proportion of Real Positive cases that are

correctly Predicted Positive. This measures the

Coverage of the Real Positive cases by the +P

(Predicted Positive) rule. Its desirable feature is that it

reflects how many of the relevant cases the +P rule

picks up. It tends not to be very highly valued in

Information Retrieval (on the assumptions that there

are many relevant documents, that it doesn't really

matter which subset we find, that we can't know

anything about the relevance of documents that aren't

returned). Recall tends to be neglected or averaged

away in Machine Learning and Computational

Linguistics (where the focus is on how confident we

can be in the rule or classifier). However, in a

Computational Linguistics/Machine Translation

context Recall has been shown to have a major

weight in predicting the success of Word Alignment

[1]. In a Medical context Recall is moreover regarded

as primary, as the aim is to identify all Real Positive

cases, and it is also one of the legs on which ROC

analysis stands. In this context it is referred to as

True Positive Rate (tpr). Recall is defined, with its

various common appellations, by equation (1):

Recall = Sensitivity = tpr = tp/rp

= TP / RP = A /(A+C) (1)

Conversely, Precision or Confidence (as it is called in

Data Mining) denotes the proportion of Predicted

Positive cases that are correctly Real Positives. This

is what Machine Learning, Data Mining and

Information Retrieval focus on, but it is totally ignored

in ROC analysis. It can however analogously be

called True Positive Accuracy (tpa), being a

measure of accuracy of Predicted Positives in

contrast with the rate of discovery of Real Positives

(tpr). Precision is defined in (2):

Precision = Confidence =tpa=tp/pp

=TP / PP = A /(A+B) (2)

These two measures and their combinations focus

only on the positive examples and predictions,

although between them they capture some

information about the rates and kinds of errors made.

However, neither of them captures any information

about how well the model handles negative cases.

Recall relates only to the +R column and Precision

only to the +P row. Neither of these takes into

account the number of True Negatives. This also

applies to their Arithmetic, Geometric and Harmonic

Means: A, Gand F=G2/A (the F-factor or F-measure).

Note that the F1-measure effectively references the

True Positives to the Arithmetic Mean of Predicted

Positives and Real Positives, being a constructed rate

normalized to an idealized value, and expressed in

this form it is known in statistics as a Proportion of

Specific Agreement as it is a applied to a specific

class, so applied to the Positive Class, it is PS+. It

also corresponds to the set-theoretic Dice Coefficient.

The Geometric Mean of Recall and Precision (G-

measure) effectively normalizes TP to the Geometric

Mean of Predicted Positives and Real Positives, and

its Information content corresponds to the Arithmetic

Table 1. Systematic and traditional notations in a binary contingency table. Shading indicates correct

(light=green) and incorrect (dark=red) rates or counts in the contingency table.

+R

−R

+R

−R

tp

fp

pp

+P

A

B

A+B

fn

tn

pn

−P

C

D

C+D

rp

rn

1

A+C

B+D

N

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Copyright © 2011 Bioinfo Publications 39

Mean of the Information represented by Recall and

Precision.

In fact, there is in principle nothing special about the

Positive case, and we can define Inverse statistics in

terms of the Inverse problem in which we interchange

positive and negative and are predicting the opposite

case. Inverse Recall or Specificity is thus the

proportion of Real Negative cases that are correctly

Predicted Negative (3), and is also known as the True

Negative Rate (tnr). Conversely, Inverse Precision

is the proportion of Predicted Negative cases that are

indeed Real Negatives (4), and can also be called

True Negative Accuracy (tna):

Inverse Recall =tnr =tn/rn

=TN/RN =D/(B+D) (3)

Inverse Precision =tna =tn/pn

=TN/PN =D/(C+D) (4)

The inverse of F1 is not known in AI/ML/CL/IR but is

just as well known as PS+ in statistics,being the

Proportion of Specific Agreement for the class of

negatives, PS−. Note that where as F1 is advocated

in AI/ML/CL/IR as a single measure to capture the

effectiveness of a system, it still completely ignores

TN which can vary freely without affecting the

statistic. In statistics, PS+ is used in conjunction with

PS− to ensure the contingencies are completely

captured, and similarly Specificity (Inverse Recall) is

always recorded along with Sensitivity (Recall).

Rand Accuracy explicitly takes into account the

classification of negatives, and is expressible (5) both

as a weighted average of Precision and Inverse

Precision and as a weighted average of Recall and

Inverse Recall:

Accuracy =tca=tcr=tp+tn

=rp⋅tpr+rn⋅tnr =(TP+TN)/N

=pp⋅tpa+pn⋅tna =(A+D)/N (5)

Dice = F1 =tp/(tp+(fn+fp)/2)

=A/(A+(B+C)/2) (6)

=1/(1+mean(FN,FP)/TP)

Jaccard =tp/(tp+fn+fp)=TP/(N-TN)

=A/(A+B+C) = A/(N-D) (7)

=1/(1+2mean(FN,FP)/TP)

= F1 / (2 – F1)

As shown in (5) Rand Accuracy is effectively a

prevalence-weighted average of Recall and Inverse

Recall, as well as a bias-weighted average of

Precision and Inverse Precision. Whilst it does take

into account TN in the numerator, the sensitivity to

bias and prevalence is an issue since these are

independent variables, with prevalence varying as we

apply to data sampled under different conditions, and

bias being directly under the control of the system

designer (e.g. as a threshold). Similarly, we can note

that one of N,FP or FN is free to vary. Whilst it

apparently takes into account TN in the

numerator,theJaccard (or Tanimoto) similarity

coefficient uses it to heuristicallydiscount the correct

classification of negatives, but it can be written (6)

independently of FN and N in a way similar tothe

effectively equivalent Dice or PS+ or F1 (7), or in

terms of them, and so is subject to bias as FN or N is

free to vary and theyfail to capture contingencies fully

without knowing inverse statisticstoo.

Each of the above also has a complementary form

defining an error rate, of which some have specific

names and importance: Fallout or False Positive Rate

(fpr) are the proportion of Real Negatives that occur

as Predicted Positive (ring-ins); Miss Rate or False

Negative Rate (fnr) are the proportion of Real

Positives that are Predicted Negatives (false-drops).

False Positive Rate is the second of the legs on

which ROC analysis is based.

Fallout =fpr =fp/rp

=FP/RP =B/(B+D) (8)

Miss Rate =fnr =fn/rn

=FN/RN =C/(A+C) (9)

Note that FN and FP are sometimes referred to as

Type I and Type II Errors, and the rates fn and fp

as alpha and beta, respectively – referring to falsely

rejecting or accepting a hypothesis. More correctly,

these terms apply specifically to the meta-level

problem discussed later of whether the precise

pattern of counts (not rates) in the contingency table

fit the null hypothesis of random distribution rather

than reflecting the effect of some alternative

hypothesis (which is not in general the one

represented by +P→+R or -P→ -Ror both).

Note that all the measures discussed individually

leave at least two degree of freedom (plus N)

unspecified and free to control, and this leaves the

door open for bias, whilst Nis needed too for

estimating significance and power.

Prevalence, Bias, Cost & Skew

We now turn our attention to the various forms of bias

that detract from the utility of all of the above surface

measures [2]. We will first note that rp represents

the Prevalence of positive cases, RP/N, and is

assumed to be a property of the population of interest

– it may be constant, or it may vary across

subpopulations, but is regarded here as not being

under the control of the experimenter, and so we

want a prevalence independent measure. By

contrast, pp represents the (label) Bias of the model

[3], the tendency of the model to output positive

labels, PP/N, and is directly under the control of the

experimenter, who can change the model by

changing the theory or algorithm, or some parameter

or threshold, to better fit the world/population being

modeled. As discussed earlier, F-factor (or Dice or

Jaccard) effectively references tp (probability or

proportion of True Positives) to the Arithmetic Mean

of Bias and Prevalence (6-7). A common rule of

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

40 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

thumb, or even a characteristic of some algorithms, is

to parameterize a model so that Prevalence = Bias,

viz. rp = pp. Corollaries of this setting are Recall

= Precision (= Dice but not Jaccard), Inverse Recall

= Inverse Precision and Fallout = Miss Rate.

Alternate characterizations of Prevalence are in terms

of Odds[4] or Skew [5], being the Class Ratio cs=

rn/rp, recalling that by definition rp+rn = 1

and RN+RP = N. If the distribution is highly

skewed, typically there are many more negative

cases than positive, this means the number of errors

due to poor Inverse Recall will be much greater than

the number of errors due to poor Recall. Given the

cost of both False Positives and False Negatives is

equal, individually, the overall component of the total

cost due to False Positives (as Negatives) will be

much greater at any significant level of chance

performance, due to the higher Prevalence of Real

Negatives.

Note that the normalized binary contingency table

with unspecified margins has three degrees of

freedom – setting any three non−Redundant ratios

determines the rest (setting any count supplies the

remaining information to recover the original table of

counts with its four degrees of freedom). In particular,

Recall, Inverse Recall and Prevalence, or

equivalently tpr, fpr and cs, suffice to determine all

ratios and measures derivable from the normalized

contingency table, but N is also required to

determine significance. As another case of specific

interest, Precision, Inverse Precision and Bias, in

combination, suffice to determine all ratios or

measures, although we will show later that an

alternate characterization of Prevalence and Bias in

terms of Evenness allows for even simpler

relationships to be exposed.

We can also take into account a differential value for

positives (cp) and negatives (cn) – this can be

applied to errors as a cost (loss or debit) and/or to

correct cases as a gain (profit or credit), and can be

combined into a single Cost Ratio cv= cn/cp.

Note that the value and skew determined costs have

similar effects, and may be multiplied to produce a

single skew-like cost factor c = cvcs. Formulations

of measures that are expressed using tpr, fpr and cs

may be made cost-sensitive by using c = cvcs in

place of c = c

s, or can be made skew/cost-

insensitive by using c = 1[5].

ROC and PN Analyses

Flach [5] highlighted the utility of ROC analysis to the

Machine Learning community, and characterized the

skew sensitivity of many measures in that context,

utilizing the ROC format to give geometric insights

into the nature of the measures and their sensitivity to

skew. [6] further elaborated this analysis, extending it

to the unnormalized PN variant of ROC, and targeting

their analysis specifically to rule learning. We will not

examine the advantages of ROC analysis here, but

will briefly explain the principles and recapitulate

some of the results.

ROC analysis plots the rate tpr against the rate

fpr, whilst PN plots the unnormalized TP against

FP. This difference in normalization only changes the

scales and gradients, and we will deal only with the

normalized form of ROC analysis. A perfect classifier

will score in the top left hand corner

(fpr=0,tpr=100%). A worst case classifier will

score in the bottom right hand corner

(fpr=100%,tpr=0). A random classifier would

be expected to score somewhere along the positive

diagonal (tpr=fpr) since the model will throw up

positive and negative examples at the same rate

(relative to their populations – these are Recall-like

scales: tpr = Recall, 1-fpr = Inverse Recall).

For the negative diagonal (tpr+c⋅fpr=1)

corresponds to matching Bias to Prevalence for a

skew of c.

The ROC plot allows us to compare classifiers

(models and/or parameterizations) and choose the

one that is closest to (0,1) and furtherest from

tpr=fpr in some sense. These conditions for

choosing the optimal parameterization or model are

not identical, and in fact the most common condition

is to minimize the area under the curve (AUC), which

for a single parameterization of a model is defined by

Figure 1. Illustration of ROC Analysis. The main

diagonal represents chance with parallel isocost lines

representing equal cost-performance. Points above

the diagonal represent performance better than

chance, those below worse than chance. For a single

good (dotted=green) system, AUC is area under curve

(trapezoid between green line and x=[0,1] ).

The perverse (dashed=red) system shown is the same

(good) system with class labels reversed.

best

tpr

fpr

worst

good

perverse

sse0

B

sse1

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Copyright © 2011 Bioinfo Publications 41

a single point and the segments connecting it to (0,0)

and (1,1). For a parameterized model it will be a

monotonic function consisting of a sequence of

segments from (0,0) to (1,1). A particular cost model

and/or accuracy measure defines an isocost gradient,

which for a skew and cost insensitive model will be

c=1, and hence another common approach is to

choose a tangent point on the highest isocost line that

touches the curve. The simple condition of choosing

the point on the curve nearest the optimum point (0,1)

is not commonly used, but this distance to (0,1) is

given by √[(-fpr)2+ (1-tpr)2], and

minimizing this amounts to minimizing the sum of

squared normalized error, fpr2+fnr2.

A ROC curve with concavities can also be locally

interpolated to produce a smoothed model following

the convex hull of the original ROC curve. It is even

possible to locally invert across the convex hull to

repair concavities, but this may overfit and thus not

generalize to unseen data. Such repairs can lead to

selecting an improved model, and the ROC curve can

also be used to return a model to changing

Prevalence and costs. The area under such a

multipoint curve is thus of some value, but the

optimum in practice is the area under the simple

trapezoid defined by the model:

AUC = (tpr-fpr+1)/2

= (tpr+tnr)/2

= 1 – (fpr+fnr)/2 (10)

For the cost and skew insensitive case, with c=1,

maximizing AUC is thus equivalent to maximizing

tpr-fpr or minimizing a sum of (absolute)

normalized error fpr+fnr. The chance line

corresponds to tpr-fpr=0, and parallel isocost

lines for c=1 have the form tpr-fpr=k. The

highest isocost line also maximizes tpr-fpr and

AUC so that these two approaches are equivalent.

Minimizing a sum of squared normalized error,

fpr2+fnr2, corresponds to a Euclidean distance

minimization heuristic that is equivalent only under

appropriate constraints, e.g. fpr=fnr, or

equivalently, Bias=Prevalence, noting that all cells are

non-negative by construction.

We now summarize relationships between the various

candidate accuracy measures as rewritten [5,6] in

terms of tpr, fpr and the skew, c, as well in

terms of Recall, Bias and Prevalence:

Accuracy = [tpr+c·(1-fpr)]/[1+c]

= 2·Recall·Prev+1-Bias−Prev (11)

Precision = tpr/[tpr+c·fpr]

= Recall·Prev/Bias (12)

F-Measure F1 = 2·tpr/[tpr+c·fpr+1]

= 2·Recall·Prev/[Bias+Prev] (13)

WRacc = 4c·[tpr-fpr]/[1+c]2

= 4·[Recall-Bias]·Prev (14)

The last measure, Weighted Relative Accuracy, was

defined [7] to subtract off the component of the True

Positive score that is attributable to chance and

rescale to the range ±1. Note that maximizingWRacc

is equivalent to maximizing AUC or tpr-fpr

=2·AUC−1, as c is constant. Thus WRAcc is an

unbiased accuracy measure, and the skew-

insensitive form of WRAcc, with c=1, is precisely

tpr-fpr. Each of the other measures (10−12)

shows a bias in that it can not be maximized

independent of skew, although skew-insensitive

versions can be defined by setting c=1. The

recasting of Accuracy, Precision and F-Measure in

terms of Recall makes clear how all of these vary only

in terms of the way they are affected by Prevalence

and Bias.

Prevalence is regarded as a constant of the target

condition or data set (and c=[1−Prev]/Prev),

whilst parameterizing or selecting a model can be

viewed in terms of trading off tpr and fpr as in

ROC analysis, or equivalently as controlling the

relative number of positive and negative predictions,

namely the Bias, in order to maximize a particular

accuracy measure (Recall, Precision, F-Measure,

Rand Accuracy and AUC). Note that for a given

Recall level, the other measures (10−13) all decrease

with increasing Bias towards positive predictions.

DeltaP, Informedness and Markedness

Powers [4] also derived an unbiased accuracy

measure to avoid the bias of Recall, Precision and

Accuracy due to population Prevalence and label

bias. The Bookmaker algorithm costs wins and losses

in the same way a fair bookmaker would set prices

based on the odds. Powers then defines the concept

of Informedness which represents the 'edge' a punter

has in making his bet, as evidenced and quantified by

his winnings. Fair pricing based on correct odds

should be zero sum – that is, guessing will leave you

with nothing in the long run, whilst a punter with

certain knowledge will win every time. Informedness

is the probability that a punter is making an informed

bet and is explained in terms of the proportion of the

time the edge works out versus ends up being pure

guesswork. Powers defined Bookmaker

Informedness for the general, K-label, case, but we

will defer discussion of the general case for now and

present a simplified formulation of Informedness, as

well as the complementary concept of Markedness.

Definition 1

Informedness quantifies how informed a

predictor is for the specified condition, and

specifies the probability that a prediction is

informed in relation to the condition (versus

chance).

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

42 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

Definition 2

Markedness quantifies how marked a

condition is for the specified predictor, and

specifies the probability that a condition is

marked by the predictor (versus chance).

These definitions are aligned with the psychological

and linguistic uses of the terms condition and marker.

The condition represents the experimental outcome

we are trying to determine by indirect means. A

marker or predictor (cf. biomarker or neuromarker)

represents the indicator we are using to determine

the outcome. There is no implication of causality –

that is something we will address later. However there

are two possible directions of implication we will

address now. Detection of the predictor may reliably

predict the outcome, with or without the occurrence of

a specific outcome condition reliably triggering the

predictor.

For the binary case we have

Informedness = Recall + Inverse Recall – 1

= tpr-fpr = 1-fnr-fpr (15)

Markedness = Precision + Inverse Precision – 1

= tpa-fna = 1-fpa-fna

We noted above that maximizing AUC or the

unbiased WRAcc measure effectively maximized tpr-

fpr and indeed WRAcc reduced to this in the skew

independent case. This is not surprising given both

Powers [4] and Flach [5-7] set out to produce an

unbiased measure, and the linear definition of

Informedness will define a unique linear form. Note

that while Informedness is a deep measure of how

consistently the Predictor predicts the Outcome by

combining surface measures about what proportion of

Outcomes are correctly predicted, Markedness is a

deep measure of how consistently the Outcome has

the Predictor as a Marker by combining surface

measures about what proportion of Predictions are

correct.

In the Psychology literature, Markedness is known as

DeltaP and is empirically a good predictor of human

associative judgements – that is it seems we develop

associative relationships between a predictor and an

outcome when DeltaP is high, and this is true even

when multiple predictors are in competition [8]. In the

context of experiments on information use in syllable

processing, [9] notes that Schanks [8] sees DeltaP as

"the normative measure of contingency", but propose

a complementary, backward, additional measure of

strength of association, DeltaP' aka dichotomous

Informedness. Perruchet and Peeremant [9] also

note the analog of DeltaP to regression coefficient,

and that the Geometric Mean of the two measures is

a dichotomous form of the Pearson correlation

coefficient, the Matthews' Correlation Coefficient,

which is appropriate unless a continuous scale is

being measured dichotomously in which case a

Tetrachoric Correlation estimate would be appropriate

[10,11].

Causality, Correlation and Regression

In a linear regression of two variables, we seek to

predict one variable, y, as a linear combination of the

other, x, finding a line of best fit in the sense of

minimizing the sum of squared error (in y). The

equation of fit has the form

y= y0 + rx·x where

rx= [n∑x·y-∑x·∑y]/[n∑x2-∑x·∑x] (16)

Substituting in counts from the contingency table, for

the regression of predicting +R (1) versus-R (0)

given +P (1) versus-P (0), we obtain this gradient of

best fit (minimizing the error in the real values R):

rP = [AD–BC] / [(A+B)(C+D)]

= A/(A+B) – C/(C+D)

= DeltaP = Markedness (17)

Conversely, we can find the regression coefficient for

predicting P from R (minimizing the error in the

predictions P):

rR = [AD–BC] / [(A+C)(B+D)]

= A/(A+C) – B/(B+D)

= DeltaP' = Informedness (18)

Finally we see that the Matthews correlation, a

contingency matrix method of calculating the Pearson

product-moment correlation coefficient, ρ, is defined

by

rG =[AD–BC]/√[(A+C)(B+D)(A+B)(C+D)]

=Correlation

=±√[Informedness·Markedness] (19)

Given the regressions find the same line of best fit,

these gradients should be reciprocal, defining a

perfect Correlation of 1. However, both Informedness

and Markedness are probabilities with an upper

bound of 1, so perfect correlation requires perfect

regression. The squared correlation is a coefficient of

proportionality indicating the proportion of the

variance in R that is explained by P, and is

traditionally also interpreted as a probability. We can

now interpret it either as the joint probability that P

informs R and R marks P, given that the two

directions of predictability are independent, or as the

probability that the variance is (causally) explained

reciprocally. The sign of the Correlation will be the

same as the sign of Informedness and Markedness

and indicates whether a correct or perverse usage of

the information has been made – take note in

interpreting the final part of (19).

Psychologists traditionally explain DeltaP in terms of

causal prediction, but it is important to note that the

direction of stronger prediction is not necessarily the

direction of causality, and the fallacy of abductive

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reasoning is that the truth of A → B does not in

general have any bearing on the truth of B → A.

If Pi is one of several independent possible causes

of R, Pi→R is strong, but R →Pi is in general

weak for any specific Pi. If Pi is one of several

necessary contributing factors to R, Pi→R is weak

for any single Pi, but R →Pi is strong. The

directions of the implication are thus not in general

dependent.

In terms of the regression to fit R from P, since there

are only two correct points and two error points, and

errors are calculated in the vertical (R) direction only,

all errors contribute equally to tilting the regression

down from the ideal line of fit. This Markedness

regression thus provides information about the

consistency of the Outcome in terms of having the

Predictor as a Marker – the errors measured from the

Outcome R relate to the failure of the Marker P to be

present.

We can gain further insight into the nature of these

regression and correlation coefficients by reducing

the top and bottom of each expression to probabilities

(dividing by N2, noting that the original contingency

counts sum to N, and the joint probabilities after

reduction sum to 1). The numerator is the

determinant of the contingency matrix, and common

across all three coefficients, reducing to dtp, whilst

the reduced denominator of the regression

coefficients depends only on the Prevalence or Bias

of the base variates. The regression coefficients,

Bookmaker Informedness (B) and Markedness (M),

may thus be re-expressed in terms of Precision (Prec)

or Recall, along with Bias and Prevalence (Prev) or

their inverses (I-):

M = dtp/ [Bias · (1-Bias)]

= dtp/ [pp·pn] = dtp / pg2

= dtp / BiasG2 = dtp / EvennessP

= [Precision – Prevalence] / IBias (20)

B = dtp/ [Prevalence · (1−Prevalence)]

= dtp/ [rp·rn] = dtp / rg2

= dtp / PrevG2= dtp / EvennessR

= [Recall – Bias] / IPrev

= Recall – Fallout

= Recall + IRecall – 1

= Sensitivity + Specificity – 1

= (LR–1)· (1–Specificity)

= (1–NLR)· Specificity

= (LR –1)· (1–NLR) / (LR–NLR) (21)

In the medical and behavioural sciences, the

Likelihood Ratio is LR=Sensitivity/[1–Specificity], and

the Negative Likelihood Ratio is NLR=Specificity/[1–

Sensitivity]. For non-negative B, LR>1>NLR, with 1

as the chance case. We also express Informedness

in these terms in (21).

The Matthews/Pearson correlation is expressed in

reduced form as the Geometric Mean of Bookmaker

Informedness and Markedness, abbreviating their

product as BookMark (BM) and recalling that it is

BookMark that acts as a probability-like coefficient of

determination, not its root, the Geometric Mean

(BookMarkG or BMG):

BMG = dtp/ √[Prev · (1−Prev)· Bias· (1-Bias)]

= dtp / [PrevG · BiasG]

= dtp / EvennessG

=√[(Recall−Bias)(Prec−Prev)]/(IPrev·IBias) (22)

These equations clearly indicate how the Bookmaker

coefficients of regression and correlation depend only

on the proportion of True Positives and the

Prevalence and Bias applicable to the respective

variables. Furthermore, Prev · Bias represents the

Expected proportion of True Positives (etp) relative

to N, showing that the coefficients each represent the

proportion of Delta True Positives (deviation from

expectation, dtp=tp-etp) renormalized in

different ways to give different probabilities.

Equations (20-22) illustrate this, showing that these

coefficients depend only on dtp and either

Prevalence, Bias or their combination. Note that for a

particular dtp these coefficients are minimized when

the Prevalence and/or Bias are at the evenly biased

0.5 level, however in a learning or parameterization

context changing the Prevalence or Bias will in

general change both tp and etp, and hence can

change dtp.

It is also worth considering further the relationship of

the denominators to the Geometric Means, PrevG of

Prevalence and Inverse Prevalence (IPrev = 1−Prev

is Prevalence of Real Negatives) and BiasG of Bias

and Inverse Bias (IBias = 1−Bias is bias to Predicted

Negatives). These Geometric Means represent the

Evenness of Real classes (EvennessR = PrevG2) and

Predicted labels (EvennessP = BiasG2). We also

introduce the concept of Global Evenness as the

Geometric Mean of these two natural kinds of

Evenness, EvennessG. From this formulation we can

see that for a given relative delta of true positive

prediction above expectation (dtp), the correlation is

at minimum when predictions and outcomes are both

evenly distributed (√EvennessG = √Evenness

R =

√EvennessP = Prev = Bias = 0.5), and Markedness and

Bookmaker are individually minimal when Bias resp.

Prevalence are evenly distributed (viz. Bias resp.

Prev = 0.5). This suggests that setting Learner Bias

(and regularized, cost-weighted or subsampled

Prevalence) to 0.5, as sometimes performed in

Artificial Neural Network training is in fact

inappropriate on theoretical grounds, as has

Previously been shown both empirically and based on

Bayesian principles – rather it is best to use

Learner/Label Bias = Natural Prevalence which is in

general much less than 0.5 [12].

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44 Journal of Machine Learning Technology

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Note that in the above equations (20-22) the

denominator is always strictly positive since we have

occurrences and predictions of both Positives and

Negatives by earlier assumption, but we note that if in

violation of this constraint we have a degenerate case

in which there is nothing to predict or we make no

effective prediction, then tp=etp and dtp=0, and

all the above regression and correlation coefficients

are defined in the limit approaching zero. Thus the

coefficients are zero if and only if dtp is zero, and

they have the same sign as dtp otherwise.

Assuming that we are using the model the right way

round, then dtp, B and M are non-negative, and

BMG is similarly non-negative as expected. If the

model is the wrong way round, then dtp, B, M and

BMG can indicate this by expressing below chance

performance, negative regressions and negative

correlation, and we can reverse the sense of P to

correct this.

The absolute value of the determinant of the

contingency matrix, dp= dtp, in these probability

formulae (20-22), also represents the sum of absolute

deviations from the expectation represented by any

individual cell and hence 2dp=2DP/N is the total

absolute relative error versus the null hypothesis.

Additionally it has a geometric interpretation as the

area of a trapezoid in PN-space, the unnormalized

variant of ROC [6].

We already observed that in (normalized) ROC

analysis, Informedness is twice the triangular area

between a positively informed system and the chance

line, and it thus corresponds to the area of the

trapezoid defined by a system (assumed to perform

no worse than chance), and any of its perversions

(interchanging prediction labels but not the real

classes, or vice-versa, so as to derive a system that

performs no better than chance), and the endpoints of

the chance line (the trivial cases in which the system

labels all cases true or conversely all are labelled

false). Such a kite-shaped area is delimited by the

dotted (system) and dashed (perversion) lines in Fig.

1 (interchanging class labels), but the alternate

parallelogram (interchanging prediction labels) is not

shown. The Informedness of a perverted system is

the negation of the Informedness of the correctly

polarized system.

We now also express the Informedness and

Markedness forms of DeltaP in terms of deviations

from expected values along with the Harmonic mean

of the marginal cardinalities of the Real classes or

Predicted labels respectively, defining DP,

DELTAP, RH, PH and related forms in terms of

their N−Relative probabilistic forms defined as

follows:

etp = rp · pp; etn = rn· pn (23)

dp = tp – etp = dtp

= -dtn = -(tn – etn)

deltap = dtp – dtn = 2dp (24)

rh = 2rp·rn / [rp+rn] = rp2/ra2

ph = 2pp·pn / [pp+pn] = pp2/pa2 (25)

DeltaP' or Bookmaker Informedness may now be

expressed in terms of deltap and rh, and DeltaP

or Markedness analogously in terms of deltap and

ph:

B = DeltaP' = [etp+dtp]/rp–[efp-dtp]/rn

= etp/rp – efp/rn + 2dtp/rh

= 2dp/rh = deltap/rh (26)

M = DeltaP = 2dp/ph = deltap/ph (27)

These harmonic relationships connect directly with

the previous geometric evenness terms by observing

HarmonicMean = GeometricMean2/ArithmeticMean

as seen in (25) and used in the alternative

expressions for normalization for Evenness in (26-

27). The use of HarmonicMean makes the

relationship with F-measure clearer, but use of

GeometricMean is generally preferred as a consistent

estimate of central tendency that more accurately

estimates the mode for skewed (e.g. Poisson) data

bounded below by 0 and unbounded above, and as

the central limit of the family of Lp based averages.

Viz. the Geometric (L0) Mean is the Geometric Mean

of the Harmonic (L−1) and Arithmetic (L+1) Means, with

positive values of p being biased higher (toward

L+∞=Max) and negative values of p being biased

lower (toward L−∞=Min).

Effect of Bias and Prev on Recall and Precision

The final form of the equations (26-27) cancels out

the common Bias and Prevalence (Prev) terms, that

denormalizedtp to tpr (Recall) or tpa (Precision).

We now recast the Bookmaker Informedness and

Markedness equations to show Recall and Precision

as subject (28-29), in order to explore the affect of

Bias and Prevalence on Recall and Precision, as well

as clarify the relationship of Bookmaker and

Markedness to these other ubiquitous but iniquitous

measures.

Recall = Bookmaker (1−Prevalence) + Bias

Bookmaker = (Recall-Bias)/(1−Prevalence) (28)

Precision = Markedness (1-Bias) + Prevalence

Markedness = (Precision−Prevalence)/(1-Bias) (29)

Bookmaker and Markedness are unbiased estimators

of above chance performance (relative to respectively

the predicting conditions or the predicted markers).

Equations (28-29) clearly show the nature of the bias

introduced by both Label Bias and Class Prevalence.

If operating at chance level, both Bookmaker and

Markedness will be zero, and Recall, Precision, and

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Copyright © 2011 Bioinfo Publications 45

derivatives such as the F-measure, will be skewed by

the biases. Note that increasing Bias or decreasing

Prevalence increases Recall and decreases

Precision, for a constant level of unbiased

performance. We can more specifically see that the

regression coefficient for the prediction of Recall from

Prevalence is −Informedness, and from Bias is +1,

and similarly the regression coefficient for the

prediction of Precision from Bias is −Markedness,

and from Prevalence is +1. Using the heuristic of

setting Bias = Prevalence then sets Recall =

Precision = F1 and Bookmaker Informedness =

Markedness = Correlation. Setting Bias = 1

(Prevalence<1) may be seen to make Precision track

Prevalence with Recall = 1, whilst Prevalence = 1

(Bias<1) means Recall = Bias with Informedness = 1,

and under either condition no information is utilized

(Bookmaker Informedness = Markedness = 0).

In summary, Recall reflects the Bias plus a

discounted estimation of Informedness and Precision

reflects the Prevalence plus a discounted estimation

of Markedness. Given usually Prevalence << ½ and

Bias << ½, their complements Inverse Prevalence >>

½ and Inverse Bias >> ½ represent substantial

weighting up of the true unbiased performance in

both these measures, and hence also in F1. High

Bias drives Recall up strongly and Precision down

according to the strength of Informedness; high

Prevalence drives Precision up and Recall down

according to the strength of Markedness.

Alternately, Informedness can be viewed (21) as a

renormalization of Recall after subtracting off the

chance level of Recall, Bias, and Markedness (20)

can be seen as a renormalization of Precision after

subtracting off the chance level of Precision,

Prevalence (and Flach’s WRAcc, the unbiased form

being equivalent to Bookmaker Informedness, was

defined in this way as discussed in §2.3).

Informedness can also be seen (21) as a

renormalization of LR or NLR after subtracting off

their chance level performance. The Kappa measure

[13-16] commonly used in assessor agreement

evaluation was similarly defined as a renormalization

of Accuracy after subtracting off an estimate of the

expected Accuracy, for Cohen Kappa being the dot

product of the Biases and Prevalences, and

expressible as a normalization of the discriminant of

contingency, dtp, by the mean error rate (cf. F1;

viz. Kappa is dtp/[dtp+mean(fp,fn)]). All three

measures are invariant in the sense that they are

properties of the contingency tables that remain

unchanged when we flip to the Inverse problem

(interchange positive and negative for both conditions

and predictions). That is we observe:

Inverse Informedness = Informedness,

Inverse Markedness = Markedness,

Inverse Kappa = Kappa.

The Dual problem (interchange antecedent and

consequent) reverses which condition is the predictor

and the predicted condition, and hence interchanges

Precision and Recall, Prevalence and Bias, as well as

Markedness and Informedness. For cross-evaluator

agreement, both Informedness and Markedness are

meaningful although the polarity and orientation of the

contingency is arbitrary. Similarly when examining

causal relationships (conventionally DeltaP vs

DeltaP'), it is useful to evaluate both deductive and

abductive directions in determining the strength of

association. For example, the connection between

cloud and rain involves cloud as one causal

antecedent of rain (but sunshowers occur

occasionally), and rain as one causal consequent of

cloud (but cloudy days aren't always wet) – only once

we have identified the full causal chain can we reduce

to equivalence, and lack of equivalence may be a

result of unidentified causes, alternate outcomes or

both.

The Perverse systems (interchanging the labels on

either the predictions or the classes, but not both)

have similar performance but occur below the chance

line (since we have assumed strictly better than

chance performance in assigning labels to the given

contingency matrix).

Note that the effect of Prevalence on Accuracy,

Recall and Precision has also been characterized

above (§2.3) in terms of Flach's demonstration of how

skew enters into their characterization in ROC

analysis, and effectively assigns different costs to

(False) Positives and (False) Negatives. This can be

controlled for by setting the parameter c

appropriately to reflect the desired skew and cost

tradeoff, with c=1 defining skew and cost insensitive

versions. However, only Informedness (or

equivalents such as DeltaP' and skew-insensitive

WRAcc) precisely characterizes the probability with

which a model informs the condition, and conversely

only Markedness (or DeltaP) precisely characterizes

the probability that a condition marks (informs) the

predictor. Similarly, only the Correlation (aka

Coefficient of Proportionality aka Coefficient of

Determination aka Squared Matthews Correlation

Coefficient) precisely characterizes the probability

that condition and predictor inform/mark each other,

under our dichotomous assumptions. Note the

Tetrachoric Correlation is another estimate of the

Pearson Correlation made under the alternate

assumption of an underlying continuous variable

(assumed normally distributed), and is appropriate if

we instead assume that we are dichotomizing a

normal continuous variable [11]. But in this article we

are making the explicit assumption that we are

dealing with a right/wrong dichotomy that is

intrinsically discontinuous.

Although Kappa does attempt to renormalize a

debiased estimate of Accuracy, and is thus much

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

46 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

more meaningful than Recall, Precision, Accuracy,

and their biased derivatives, it is intrinsically non-

linear, doesn't account for error well, and retains an

influence of bias, so that there does not seem that

there is any situation when Kappa would be

preferable to Correlation as a standard independent

measure of agreement [16,13]. As we have seen,

Bookmaker Informedness, Markedness and

Correlation reflect the discriminant of relative

contingency normalized according to different

Evenness functions of the marginal Biases and

Prevalences, and reflect probabilities relative to the

corresponding marginal cases. However, we have

seen that Kappa scales the discriminant in a way that

reflects the actual error without taking into account

expected error due to chance, and in effect it is really

just using the discriminant to scale the actual mean

error: Kappa is dtp/[dtp+mean(fp,fn)] =

1/[1+mean(fp,fn)/dtp] which approximates for

small error to 1-mean(fp,fn)/dtp.

The relatively good fit of Kappa to Correlation and

Informedness is illustrated in Fig. 2, along with the

poor fit of the Rank Weighted Average and the

Geometric and Harmonic (F-factor) means. The fit of

the Evenness weighted determinant is perfect and not

easily distinguishable but the separate components

(Determinant and geometric means of Real

Prevalences and Prediction Biases) are also shown

(+1 for clarity).

Significance and Information Gain

The ability to calculate various probabilities from a

contingency table says nothing about the significance

of those numbers – is the effect real, or is it within the

expected range of variation around the values

expected by chance? Usually this is explored by

considering deviation from the expected values (ETP

and its relatives) implied by the marginal counts (RP,

PP and relatives) – or from expected rates implied by

the biases (Class Prevalence and Label Bias). In the

case of Machine Learning, Data Mining, or other

artificially derived models and rules, there is the

further question of whether the training and

parameterization of the model has set the 'correct' or

'best' Prevalence and Bias (or Cost) levels.

Furthermore, should this determination be undertaken

by reference to the model evaluation measures

(Recall, Precision, Informedness, Markedness and

their derivatives), or should the model be set to

maximize the significance of the results?

This raises the question of how our measures of

association and accuracy, Informedness, Markedness

and Correlation, relate to standard measures of

significance.

This article has been written in the context of a

Prevailing methodology in Computational Linguistics

and Information Retrieval that concentrates on target

positive cases and ignores the negative case for the

purpose of both measures of association and

significance. A classic example is saying “water” can

only be a noun because the system is inadequate to

the task of Part of Speech identification and this

boosts Recall and hence F-factor, or at least setting

the Bias to nouns close to 1, and the Inverse Bias to

verbs close to 0. Of course, Bookmaker will then be

0 and Markedness unstable (undefined, and very

sensitive to any words that do actually get labelled

verbs). We would hope that significance would also

Figure 2. Accuracy of traditional measures.

110 Monte Carlo simulations with 11 stepped

expected Informedness levels (red) with Bookmaker-

estimated Informedness (red dot), Markedness (green

dot) and Correlation (blue dot), and showing (dashed)

Kappa versus the biased traditional measures Rank

Weighted Average (Wav), Geometric Mean (Gav) and

Harmonic Mean F1 (Fav). The Determinant (D) and

Evenness k-th roots (gR=PrevG and gP=BiasP) are

shown +1. K=4, N=128.

(Online version has figures in colour.)

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Copyright © 2011 Bioinfo Publications 47

be 0 (or near zero given only a relatively small

number of verb labels). We would also like to be able

to calculate significance based on the positive case

alone, as either the full negative information is

unavailable, or it is not labelled.

Generally when dealing with contingency tables it is

assumed that unused labels or unrepresented

classes are dropped from the table, with

corresponding reduction of degrees of freedom. For

simplicity we have assumed that the margins are all

non-zero, but the freedoms are there whether they

are used or not, so we will not reduce them or reduce

the table.

There are several schools of thought about

significance testing, but all agree on the utility of

calculating a p-value [19], by specifying some statistic

or exact test T(X) and setting p = Prob(T(X) ≥

T(Data)). In our case, the Observed Data is

summarized in a contingency table and there are a

number of tests which can be used to evaluate the

significance of the contingency table.

For example, Fisher's exact test calculates the

proportion of contingency tables that are at least as

favourable to the Prediction/Marking hypothesis,

rather than the null hypothesis, and provides an

accurate estimate of the significance of the entire

contingency table without any constraints on the

values or distribution. The log-likelihood-based G2

test and Pearson's approximating χ2 tests are

compared against a Chi-Squared Distribution of

appropriate degree of freedom (r=1 for the binary

contingency table given the marginal counts are

known), and depend on assumptions about the

distribution, and may focus only on the Predicted

Positives.

χ2 captures the Total Squared Deviation relative to

expectation, is here calculated only in relation to

positive predictions as often only the overt prediction

is considered, and the implicit prediction of negative

case is ignored [17-19], noting that it sufficient to

count r=1 cells to determine the table and make a

significance estimate. However, χ2 is valid only for

reasonably sized contingencies (one rule of thumb is

that the expectation for the smallest cell is at least 5,

and the Yates and Williams corrections will be

discussed in due course [18,19]):

χ2+P = (TP-ETP)2/ETP+(FP-EFP)2/EFP

= DTP2/ETP + DFP2/EFP

= 2DP2/EHP, EHP

= 2ETP·EFP/[ETP+EFP]

= 2N·dp2/ehp,ehp

= 2etp·efp/[etp+efp]

= 2N·dp2/[rh·pp]= N·dp2/PrevG2/Bias

= N·B2·EvennessR/Bias = N·r2P·PrevG2/Bias

≈ (N+PN)·r2P·PrevG2 (Bias → 1)

= (N+PN)·B2·EvennessR (30)

G2 captures Total Information Gain, being N times the

Average Information Gain in nats, otherwise known

as Mutual Information, which however is normally

expressed in bits. We will discuss this separately

under the General Case. We deal with G2 for positive

predictions in the case of small effect, that is dp

close to zero, showing that G2is twice as sensitive as

χ2 in this range.

G2+P/2=TP·ln(TP/ETP) + FP·ln(FP/EFP)

=TP·ln(1+DTP/ETP)+FP·ln(1+DFP/EFP)

≈ TP·(DTP/ETP) + FP·(DFP/EFP)

= 2N·dp2/ehp

= 2N·dp2/[rh·pp]

= N·dp2/PrevG2/Bias

= N·B2·EvennessR/Bias

= N·r2P·PrevG2/Bias

≈ (N+PN)·r2P·PrevG2 (Bias → 1)

= (N+PN)·B2·EvennessR (31)

In fact χ2 is notoriously unreliable for small N and

small cell values, and G2 is to be preferred. The Yates

correction (applied only for cell values under 5) is to

subtract 0.5 from the absolute dp value for that cell

before squaring completing the calculation [17-19].

Our result (30-1) shows that χ2 and G2 significance of

the Informedness effect increases with N as

expected, but also with the square of Bookmaker, the

Evenness of Prevalence (EvennessR = PrevG2 =

Prev·(1−Prev)) and the number of Predicted

Negatives (viz. with Inverse Bias)! This is as

expected. The more Informed the contingency

regarding positives, the less data will be needed to

reach significance. The more Biased the contingency

towards positives, the less significant each positive is

and the more data is needed to ensure significance.

The Bias-weighted average over all Predictions (here

for K=2 case: Positive and Negative) is simply

KN·B2·PrevG2 which gives us an estimate of the

significance without focussing on either case in

particular.

χ2

KB = 2N·dtp2/PrevG2

= 2N·rP2 ·PrevG2

= 2N·rP2 ·EvennessR

= 2N·B2·EvennessR (32)

Analogous formulae can be derived for the

significance of the Markedness effect for positive real

classes, noting that EvennessP = BiasG2 .

χ2

KM = 2N·dtp2/BiasG2

= 2N ·rR2 · BiasG2

= 2N·M2·EvennessP (33)

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

48 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

The Geometric Mean of these two overall estimates

for the full contingency table is

χ2

KBM = 2N·dtp2/PrevG·BiasG

= 2N·rP·rR ·PrevG·BiasG

= 2N·r2

G·EvennessG= 2Nρ2·EvennessG

= 2N·B·M ·EvennessG (34)

This is simply the total Sum of Squares Deviance

(SSD) accounted for by the correlation coefficient

BMG (22) over the N data points discounted by the

Global Evenness factor, being the squared Geometric

Mean of all four Positive and Negative Bias and

Prevalence terms (EvennessG= PrevG·BiasG). The

less even the Bias and Prevalence, the more data will

be required to achieve significance, the maximum

evenness value of 0.25 being achieved with both

even bias and even Prevalence. Note that for even

bias or Prevalence, the corresponding positive and

negative significance estimates match the global

estimate.

When χ2+P or G2+P is calculated for a specific label in

a dichotomous contingency table, it has one degree

of freedom for the purposes of assessment of

significance. The full table also has one degree of

freedom, and summing for goodness of fit over only

the positive prediction label will clearly lead to a lower

χ2 estimate than summing across the full table, and

while summing for only the negative label will often

give a similar result it will in general be different. Thus

the weighted arithmetic mean calculated by χ2

KB is

an expected value independent of the arbitrary choice

of which predictive variate is investigated. This is

used to see whether a hypothesized main effect (the

alternate hypothesis, HA) is borne out by a significant

difference from the usual distribution (the null

hypothesis, H0). Summing over the entire table

(rather than averaging of labels), is used for χ2 or G2

independence testing independent of any specific

alternate hypothesis [21], and can be expected to

achieve a χ2 estimate approximately twice that

achieved by the above estimates, effectively

cancelling out the Evenness term, and is thus far less

conservative (viz. it is more likely to satisfy p<α):

χ2

BM = N·r2

G= N·ρ2= N·φ2= N·B·M (35)

Note that this equates Pearson’s Rho, ρ, with the Phi

Correlation Coefficient, φ, which is defined in terms of

the Inertia φ2=χ2/N. We now have confirmed that not

only does a factor of N connects the full contingency

G2 to Mutual Information (MI), but it also normalizes

the full approximate χ2 contingency to

Matthews/Pearson (=BMG=Phi) Correlation, at least

for the dichotomous case. This tells us moreover, that

MI and Correlation are measuring essentially the

same thing, but MI and Phi do not tell us anything

about the direction of the correlation, but the sign of

Matthews or Pearson or BMG Correlation does (it is

the Biases and Prevalences that are multiplied and

squarerooted).

The individual or averaged goodness-of-fit estimates

are in general much more conservative than full

contingency table estimation of p by the Fisher Exact

Test, but the full independence estimate can over

inflate the statistic due to summation of more than

there are degrees of freedom. The conservativeness

has to do both with distributional assumptions of the

χ2 or G2 estimates that are only asymptotically valid

as well as the approximative nature of χ2 in particular.

Also note that α bounds the probability of the null

hypothesis, but 1-α is not a good estimate of the

probabilty of any specific alternate hypothesis. Based

on a Bayesian equal probability prior for the null

hypothesis (H0, e.g. B=M=0 as population effect) and

an unspecific one-tailed alternate hypothesis (HA, e.g.

the measured B and C as true population effect), we

can estimate new posterior probability estimates for

Type I (H0 rejection, Alpha(p)) and Type II (HA

rejection, Beta(p)) errors from the posthoc

likelihood estimation [22]:

L(p) =Alpha(p)/Beta(p)

≈ – e p log(p) (36)

Alpha(p) = 1/[1+1/L(p)] (37)

Beta(p) = 1/[1+L(p)] (38)

Confidence Intervals and Deviations

An alternative to significance estimation is confidence

estimation in the statistical rather than the data

mining sense. We noted earlier that selecting the

highest isocost line or maximizing AUC or Bookmaker

Informedness, B, is equivalent to minimizing

fpr+fnr=(1-B) or maximizing tpr+tnr=(1+B),

which maximizes the sum of normalized squared

deviations of B from chance, sseB=B2 (as is seen

geometrically from Fig. 1). Note that this contrasts

with minimizing the sum of squares distance from the

optimum which minimizes the relative sum of squared

normalized error of the aggregated contingency,

sseB=fpr2+fnr2. However, an alternate

definition calculating the sum of squared deviation

from optimum is as a normalization the square of the

minimum distance to the isocost of contingency,

sseB=(1-B)2.

This approach contrasts with the approach of

considering the error versus a specific null hypothesis

representing the expectation from margins.

Normalization is to the range [0,1] like |B| and

normalizes (due to similar triangles) all orientations of

the distance between isocosts (Fig. 1). With these

estimates the relative error is constant and the

relative size of confidence intervals around the null

and full hypotheses only depend on N as |B| and |1-

B| are already standardized measures of deviation

from null or full correlation respectively (σ/µ=1). Note

however that if the empirical value is 0 or 1, these

measures admit no error versus no information or full

information resp. If the theoretical value is B=0, then

Powers DMW

Copyright © 2011 Bioinfo Publications 49

a full ±1 error is possible, particularly in the discrete

low N case where it can be equilikely and will be more

likely than expected values that are fractional and

thus likely to become zeros. If the theoretical value is

B=1, then no variation is expected unless due to

measurement error. Thus |1-B| reflects the maximum

(low N) deviation in the absence of measurement

error.

The standard Confidence Interval is defined in terms

of the Standard Error, SE =√[SSE/(N∙(N-1))]

=√[sse/(N-1)]. It is usual to use a multiplier X of

around X=2 as, given the central limit theorem applies

and the distribution can be regarded as normal, a

multiplier of 1.96 corresponds to a confidence of 95%

that the true mean lies in the specified interval around

the estimated mean, viz. the probability that the

derived confidence interval will bound the true mean

is 0.95 and the test thus corresponds approximately

to a significance test with alpha=0.05 as the

probability of rejecting a correct null hypothesis, or a

power test with beta=0.05 as the probability of

rejecting a true full or partial correlation hypothesis. A

number of other distributions also approximate 95%

confidence at 2SE.

We specifically reject the more traditional approach

which assumes that both Prevalence and Bias are

fixed, defining margins which in turn define a specific

chance case rather than an isocost line representing

all chance cases – we cannot assume that any

solution on an isocost line has greater error than any

other since all are by definition equivalent. The above

approach is thus argued to be appropriate for

Bookmaker and ROC statistics which are based on

the isocost concept, and reflects the fact that most

practical systems do not in fact preset the Bias or

match it to Prevalence, and indeed Prevalences in

early trials may be quite different from those in the

field.

he specific estimate of sse that we present for

alpha, the probability of the current estimate for B

occurring if the true Informedness is B=0,

is√sseB0=|1-B|=1, which is appropriate for testing

the null hypothesis, and thus for defining

unconventional error bars on B=0. Conversely,

√sseB2=|B|=0, is appropriate for testing deviation

from the full hypothesis in the absence of

measurement error, whilst √sseB2=|B|=1

conservatively allows for full range measurement

error, and thus defines unconventional error bars on

B=M=C=1.

In view of the fact that there is confusion between the

use of beta in relation to a specific full dependency

hypothesis, B=1 as we have just considered, and the

conventional definition of an arbitrary and unspecific

alternate contingent hypothesis, B≠0, we designate

the probability of incorrectly excluding the full

hypothesis by gamma, and propose three possible

related kinds of correction for the √sse for beta:

some kind of mean of |B| and |1-B| (the unweighted

arithmetic mean is 1/2, the geometric mean is less

conservative and the harmonic mean least

conservative), the maximum or minimum (actually a

special case of the last, the maximum being

conservative and the minimum too low an

underestimate in general), or an asymmetric interval

that has one value on the null side and another on the

full side (a parameterized special case of the last that

corresponds to percentile-based usages like box

plots, being more appropriate to distributions that

cannot be assumed to be symmetric).

The √sse means may be weighted or unweighted

and in particular a self-weighted arithmetic mean

gives our recommended definition, √sseB1=1-

2|B|+2B2, whilst an unweighted geometric mean

gives √sseB1=√[|B|-B2] and an unweighted

harmonic mean gives √sseB1=|B|-B2. All of these

are symmetric, with the weighted arithmetic mean

giving a minimum of 0.5 at B=±0.5 and a maximum of

1 at both B=0 and B=±1, contrasting maximally with

sseB0and sseB2resp in these neighbourhoods,

whilst the unweighted harmonic and geometric means

having their minimum of 0 at both B=0 and B=±1,

acting like sseB0and sseB2resp in these

neighbourhoods (which there evidence zero variance

around their assumed true values). The minimum at

B=±0.5 for the geometric mean is 0.5 and for the

harmonic mean, 0.25.

For this probabilistic |B| range, the weighted

arithmetic mean is never less than the arithmetic

mean and the geometric mean is never more than

the arithmetic mean. These relations demonstrate the

complementary nature of the weighted/arithmetic and

unweighted geometric means. The maxima at the

extremes is arguably more appropriate in relation to

power as intermediate results should calculate

squared deviations from a strictly intermediate

expectation based on the theoretical distribution, and

will thus be smaller on average if the theoretical

hypothesis holds, whilst providing emphasized

differentiation when near the null or full hypothesis.

The minima of 0 at the extremes are not very

appropriate in relation to significance versus the null

hypothesis due the expectation of a normal

distribution, but its power dual versus the full

hypothesis is appropriately a minimum as perfect

correlation admits no error distribution. Based on

Monte Carlo simulations, we have observed that

setting sseB1=√sseB2=1-|B| as per the usual

convention is appropriately conservative on the

upside but a little broad on the downside, whilst the

weighted arithmetic mean, √sseB1=1-2|B|+2B2, is

sufficiently conservative on the downside, but

unnecessarily conservative for high B.

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

50 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

Note that these two-tailed ranges are valid for

Bookmaker Informedness and Markedness that can

go positive or negative, but a one tailed test would be

appropriate for unsigned statistics or where a

particular direction of prediction is assumed as we

have for our contingency tables. In these cases a

smaller multiplier of 1.65 would suffice, however the

convention is to use the overlapping of the confidence

bars around the various hypotheses (although usually

the null is not explicitly represented).

Thus for any two hypotheses (including the null

hypothesis, or one from a different contingency table

or other experiment deriving from a different theory or

system) the traditional approach of checking that

1.95SE (or 2SE) error bars don’t overlap is rather

conservative (it is enough for the value to be outside

the range for a two-sided test), whilst checking

overlap of 1SE error bars is usually insufficiently

conservative given that the upper represents

beta<alpha. Where it is expected that one will be

better than the other, a 1.65SE error bar including the

mean for the other hypothesis is enough to indicate

significance (or power=1-beta) corresponding to

alpha (or beta) as desired.

The traditional calculation of error bars based on Sum

of Squared Error is closely related to the calculation

of Chi-Squared significance based on Total Squared

Deviation, and like it are not reliable when the

assumptions of normality are not approximated, and

in particular when the conditions for the central limit

theorem are not satisfied (e.g. N<12 or cell-count<5).

They are not appropriate for application to

probabilistic measures of association or error. This is

captured by the meeting of the X=2 error bars for the

full (sseB2) and null (sseB0) hypotheses at N=16

(expected count of only 4 per cell).

Here we have considered only the dichotomous case

but discuss confidence intervals further below, in

relation to the general case.

SIMPLE EXAMPLES

Bookmaker Informedness has been defined as the

Probability of an informed decision, and we have

shown identity with DeltaP' and WRAcc, and the

close relationship (10, 15) with ROC AUC. A system

that makes an informed (correct) decision for a target

condition with probability B, and guesses the

remainder of the time, will exhibit a Bookmaker

Informedness (DeltaP') of B and a Recall of

B·(1−Prev) + Bias. Conversely a proposed marker

which is marked (correctly) for a target condition with

probability M, and according to chance the remainder

of the time, will exhibit a Markedness (DeltaP) of M

and a Precision of M·(1-Bias) + Prev. Precision and

Recall are thus biased by Prevalence and Bias, and

variation of system parameters can make them rise or

fall independently of Informedness and Markedness.

Accuracy is similarly dependent on Prevalence and

Bias:

2·(B·(1−Prev)·Prev+Bias·Prev)+1-(Bias+Prev),

and Kappa has an additional problem of non-linearity

due to its complex denominator:

B·(1−Prev)·Prev / (1-Bias·Prev-(Bias+Prev)/2).

It is thus useful to illustrate how each of these other

measures can run counter to an improvement in

overall system performance as captured by

Informedness. For the examples in Table 2 (for

N=100) all the other measure rise, some quite

considerably, but Bookmaker actually falls. Table 2

also illustrates the usage of the Bookmaker and

Markedness variants of the χ2 statistic versus the

standard formulation for the positive case, showing

also the full K class contingency version (for K=2 in

this case).

Note that under the distributional and approximative

assumptions for χ2 neither of these contingencies

differ sufficiently from chance at N=100 to be

Table 2. Binary contingency tables.Colour coding highlights example counts of correct (light green) and incorrect

(dark red) decisions with the resulting Bookmaker Informedness (B=WRacc=DeltaP'), Markedness (C=DeltaP),

Matthews Correlation (C), Recall, Precision, Rand Accuracy, Harmonic Mean of Recall and Precision (F=F1),

Geometric Mean of Recall and Precision (G), Cohen Kappa (κ),andχ2 calculated using Bookmaker (χ 2+P), Markedness

(χ 2+R) and standard (χ 2) methods across the positive prediction or condition only, as well as calculated across the

entire K=2 class contingency, all of which are designed to be referenced to alpha (α) according to the χ2 distribution,

with the latter more reliable due to taking into account all contingencies. Single-tailed threshold is shown for α =0.05.

68.0%

32.0%

χ2@α=0.05

3.85

76.0%

56

20

76

B

19.85%

Recall

82.35%

F

77.78%

χ2+P

1.13

χ2KB

1.72

24.0%

12

12

24

M

23.68%

Precision

73.68%

G

77.90%

χ2+R

1.61

χ2KM

2.05

68

32

100

C

21.68%

Rand Acc

68.00%

κ

21.26%

χ2

1.13

χ2KBM

1.87

60.0%

40.0%

χ2@α=0.05

3.85

42.0%

30

12

42

B

20.00%

Recall

50.00%

F

58.82%

χ2+P

2.29

χ2

KB

1.92

58.0%

30

28

58

M

19.70%

Precision

71.43%

G

59.76%

χ2

+R

2.22

χ2KM

1.89

60

40

100

C

19.85%

Rand Acc

58.00%

κ

18.60%

χ 2

2.29

χ2KBM

1.91

Powers DMW

Copyright © 2011 Bioinfo Publications 51

significant to the 0.05 level due to the low

Informedness Markedness and Correlation, however

doubling the performance of the system would suffice

to achieve significance at N=100 given the Evenness

specified by the Prevalences and/or Biases).

Moreover, even at the current performance levels the

Inverse (Negative) and Dual (Marking) Problems

show higher χ2 significance, approaching the 0.05

level in some instances (and far exceeding it for the

Inverse Dual). The KB variant gives a single

conservative significance level for the entire table,

sensitive only to the direction of proposed implication,

and is thus to be preferred over the standard versions

that depend on choice of condition.

Incidentally, the Fisher Exact Test shows significance

to the 0.05 level for both the examples in Table 2.

This corresponds to an assumption of a

hypergeometric distribution rather than normality –

viz. all assignments of events to cells are assumed to

be equally likely given the marginal constraints (Bias

and Prevalence). However it is in appropriate given

the Bias and Prevalence are not specified by the

experimenter in advance of the experiment as is

assumed by the conditions of this test. This has also

been demonstrated empirically through Monte Carlo

simulation as discussed later. See [22] for a

comprehensive discussion on issues with significance

testing, as well as Monte Carlo simulations.

PRACTICAL CONSIDERATIONS

If we have a fixed size dataset, then it is arguably

sufficient to maximize the determinant of the

unnormalized contingency matrix, DT. However this

is not comparable across datasets of different sizes,

and we thus need to normalize for N, and hence

consider the determinant of the normalized

contingency matrix, dt. However, this value is still

influenced by both Bias and Prevalence.

In the case where two evaluators or systems are

being compared with no a priori preference, the

Correlation gives the correct normalization by their

respective Biases, and is to be preferred to Kappa.

In the case where an unimpeachable Gold Standard

is employed for evaluation of a system, the

appropriate normalization is for Prevalence or

Evenness of the real gold standard values, giving

Informedness. Since this is constant, optimizing

Informedness and optimizing dtare equivalent.

More generally, we can look not only at what

proposed solution best solves a problem, by

comparing Informedness, but which problem is most

usefully solved by a proposed system. In a medical

context, for example, it is usual to come up with

potentially useful medications or tests, and then

explore their effectiveness across a wide range of

complaints. In this case Markedness may be

appropriate for the comparison of performance across

different conditions.

Recall and Informedness, as biased and unbiased

variants of the same measure, are appropriate for

testing effectiveness relative to a set of conditions,

and the importance of Recall is being increasingly

recognized as having an important role in matching

human performance, for example in Word Alignment

for Machine Translation [1]. Precision and

Markedness, as biased and unbiased variants of the

same measure, are appropriate for testing

effectiveness relative to a set of predictions. This is

particularly appropriate where we do not have an

appropriate gold standard giving correct labels for

every case, and is the primary measure used in

Information Retrieval for this reason, as we cannot

know the full set of relevant documents for a query

and thus cannot calculate Recall.

However, in this latter case of an incompletely

characterized test set, we do not have a fully

specified contingency matrix and cannot apply any of

the other measures we have introduced. Rather,

whether for Information Retrieval or Medical Trials, it

is assumed that a test set is developed in which all

real labels are reliably (but not necessarily perfectly)

assigned. Note that in some domains, labels are

assigned reflecting different levels of assurance, but

this has lead to further confusion in relation to

possible measures and the effectiveness of the

techniques evaluated [1]. In Information Retrieval,

the labelling of a subset of relevant documents

selected by an initial collection of systems can lead to

relevant documents being labelled as irrelevant

because they were missed by the first generation

systems – so for example systems are actually

penalized for improvements that lead to discovery of

relevant documents that do not contain all specified

query words. Thus here too, it is important to develop

test sets that of appropriate size, fully labelled, and

appropriate for the correct application of both

Informedness and Markedness, as unbiased versions

of Recall and Precision.

This Information Retrieval paradigm indeed provides

a good example for the understanding of the

Informedness and Markedness measures. Not only

can documents retrieved be assessed in terms of

prediction of relevance labels for a query using

Informedness, but queries can be assessed in terms

of their appropriateness for the desired documents

using Markedness, and the different kinds of search

tasks can be evaluated with the combination of the

two measures. The standard Information Retrieval

mantra that we do not need to find all relevant

documents (so that Recall or Informedness is not so

relevant) applies only where there are huge numbers

of documents containing the required information and

a small number can be expected to provide that

information with confidence. However another kind of

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

52 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

Document Retrieval task involves a specific and

rather small set of documents for which we need to

be confident that all or most of them have been found

(and so Recall or Informedness are especially

relevant). This is quite typical of literature review in a

specialized area, and may be complicated by new

developments being presented in quite different forms

by researchers who are coming at it from different

directions, if not different disciplinary backgrounds.

THE GENERAL CASE

So far we have examined only the binary case with

dichotomous Positive versus Negative classes and

labels.

It is beyond the scope of this article to consider the

continuous or multi-valued cases, although the

Matthews Correlation is a discretization of the

Pearson Correlation with its continuous-valued

assumption, and the Spearman Rank Correlation is

an alternate form applicable to arbitrary discrete value

(Likert) scales, and Tetrachoric Correlation is

available to estimate the correlation of an underlying

continuous scale [11]. If continuous measures

corresponding to Informedness and Markedness are

required due to the canonical nature of one of the

scales, the corresponding Regression Coefficients

are available.

It is however, useful in concluding this article to

consider briefly the generalization to the multi-class

case, and we will assume that both real classes and

predicted classes are categorized with K labels, and

again we will assume that each class is non-empty

unless explicitly allowed (this is because Precision is

ill-defined where there are no predictions of a label,

and Recall is ill-defined where there are no members

of a class).

Generalization of Association

Powers [4] derives Bookmaker Informedness (41)

analogously to Mutual Information & Conditional

Entropy (39-40) as a pointwise average across the

contingency cells, expressed in terms of label

probabilities PP(l), where PP(l) is the probability of

Prediction l, and label-conditioned class probabilities

PR(c|l) , where P

R(c|l) is the probability that the

Prediction labeled l is actually of Real class c, and in

particular PR(l|l) = Precision(l), and where we use the

delta functions as mathematical shorthands for

Boolean expressions interpreted algorithmically as in

C, with true expressions taking the value 1 and false

expressions 0, so that δ|c-l|≡ (c = l) represents a Dirac

measure (limit as δ→0); ∂|c-l|≡ (c ≠ l) represents its

logical complement (1 if c ≠ l and 0 if c = l)).

MI(R||P) =∑l PP(l) ∑c PR(c|l) [–log(PR(c|l))/PR(c)] (39)

H(R|P) =∑l PP(l) ∑c PR(c|l) [–log(PR(c|l))] (40)

B(R|P) =∑l PP(l) ∑c PR(c|l) [PP(l)/(PR(l) – ∂|c-l|)] (41)

We now define a binary dichotomy for each label l

with l and the corresponding c as the Positive cases

(and all other labels/classes grouped as the Negative

case). We next denote its Prevalence Prev(l) and its

dichotomous Bookmaker Informedness B(l), and so

can simplify (41) to

B(R|P) = ∑l Prev(l) B(l) (42)

Analogously we define dichotomous Bias(c) and

Markedness(c) and derive

M(P|R) = ∑c Bias(c) M(c) (43)

These formulations remain consistent with the

definition of Informedness as the probability of an

informed decision versus chance, and Markedness as

its dual. The Geometric Mean of multi-class

Informedness and Markedness would appear to give

us a new definition of Correlation, whose square

provides a well defined Coefficient of Determination.

Recall that the dichotomous forms of Markedness

(20) and Informedness (21) have the determinant of

the contingency matrix as common numerators, and

have denominators that relate only to the margins, to

Prevalence and Bias respectively. Correlation,

Markedness and Informedness are thus equal when

Prevalence = Bias. The dichotomous Correlation

Coefficient would thus appear to have three factors, a

common factor across Markedness and

Informedness, representing their conditional

dependence, and factors representing Evenness of

Bias (cancelled in Markedness) and Evenness of

Prevalence (cancelled in Informedness), each

representing a marginal independence.

In fact, Bookmaker Informedness can be driven

arbitrarily close to 0 whilst Markedness is driven

arbitrarily close to 1, demonstrating their

independence – in this case Recall and Precision will

be driven to or close to 1. The arbitrarily close hedge

relates to our assumption that all predicted and real

classes are non-empty, although appropriate limits

could be defined to deal with the divide by zero

problems associated with these extreme cases.

Technically, Informedness and Markedness are

conditionally independent – once the determinant

numerator is fixed, their values depend only on their

respective marginal denominators which can vary

independently. To the extent that they are

independent, the Coefficient of Determination acts as

the joint probability of mutual determination, but to the

extent that they are dependent, the Correlation

Coefficient itself acts as the joint probability of mutual

determination.

These conditions carry over to the definition of

Correlation in the multi-class case as the Geometric

Mean of Markedness and Informedness – once all

numerators are fixed, the denominators demonstrate

marginal independence.

Powers DMW

Copyright © 2011 Bioinfo Publications 53

We now reformulate the Informedness and

Markedness measures in terms of the Determinant of

the Contingency and Evenness, generalizing (20-22).

In particular, we note that the definition of Evenness

in terms of the Geometric Mean or product of biases

or Prevalences is consistent with the formulation in

terms of the determinants DET and det

(generalizing dichotomous DP=DTP and dp=dtp)

and their geometric interpretation as the area of a

parallelogram in PN-space and its normalization to

ROC-space by the product of Prevalences, giving

Informedness, or conversely normalization to

Markedness by the product of biases. The

generalization of DET to a volume in high

dimensional PN-space and det to its normalization

by product of Prevalences or biases, is sufficient to

guarantee generalization of (20-22) to K classes by

reducing from KD to SSD so that BMG has the form

of a coefficient of proportionality of variance:

M ≈ [det / BiasGK]2/K

= det2/K / EvennessP+ (44)

B ≈ [det / PrevGK ]2/K

= det2/K / EvennessR+ (45)

BMG ≈ det2/K / [PrevG · BiasG]

= det2/K / EvennessG+ (46)

We have marked the Evenness terms in these

equations with a trailing plus to distinguish them from

other usages, and their definitions are clear from

comparison of the denominators. Note that the

Evenness terms for the generalized regressions (44-

45) are not Arithmetic Means but have the form of

Geometric Means. Furthermore, the dichotomous

case emerges for K=2 as expected. Empirically (Fig.

3), this generalization matches well near B=0 or B=1,

but fares less well in between the extremes,

suggesting a mismatched exponent in the heuristic

conversion of K dimensions to 2. Here we set up the

Monte Carlo simulation as follows: we define the

diagonal of a random perfect performance

contingency table with expected N entries using a

random uniform distribution, we define a random

chance level contingency table setting margins

independently using a random binormal distribution,

then distributing randomly across cells around their

expected values, we combine the two (perfect and

chance) random contingency tables with respective

weights I and (1-I), and finally increment or

decrement cells randomly to achieve cardinality N

which is the expected number but is not constrained

by the process for generating the random (perfect

and chance) matrices. This procedure was used to

ensure Informedness and Markedness estimates

retain a level of independence; otherwise they tend to

correlate very highly with overly uniform margins for

higher K and lower N (conditional independence is

lost once the margins are specified) and in particular

Informedness, Markedness, Correlation and Kappa

would always agree perfectly for either I=1 or

perfectly uniform margins. Note this use of

Informedness to define a target probability of an

informed decision followed by random inclusion or

deletion of cases when there is a mismatch versus

the expected number of instances N – the preset

Informedness level is thus not a fixed preset

Informedness but a target level that permits jitter

around that level, and in particular will be an

Figure 3. Determinant-based estimates of correlation.

110 Monte Carlo simulations with 11 stepped expected

Informedness levels (red line) with Bookmaker-

estimated Informedness (red dots), Markedness (green

dot) and Correlation (blue dot), with significance (p+1)

calculated using G2, X2, and Fisher estimates, and

Correlation estimates calculated from the Determinant of

Contingency using two different exponents, 2/K (DB &

DM) and 1/[3K-2] (DBa and DMa). The difference

between the estimates is also shown.

Here K=4, N=128, X=1.96, α=β=0.05.

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

54 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

overestimate for the step I=1 (no negative counts

possible) which can be detected by excess deviation

beyond the set Confidence Intervals for high

Informedness steps.

In Fig. 3 we therefore show and compare an alternate

exponent of 1/(3K-2) rather than the exponent of 2/K

shown in (44 to 45). This also reduces to 1 and

hence the expected exact correspondence for K=2.

This suggests that what is important is not just the

number of dimensions, but the also the number of

marginal degrees of freedom: K+2(K-1), but

although it matches well for high degrees of

association it shows similar error at low informedness.

The precise relationship between Determinant and

Correlation, Informedness and Markedness for the

general case remains a matter for further

investigation. We however continue with the use of

the approximation based on 2/K.

The EvennessR (Prev.IPrev) concept corresponds to

the concept of Odds (IPrev/Prev), where

Prev+IPrev=1, and Powers [4] shows that (multi-

class) Bookmaker Informedness corresponds to the

expected return per bet made with a fair Bookmaker

(hence the name). From the perspective of a given

bet (prediction), the return increases as the

probability of winning decreases, which means that

an increase in the number of other winners can

increase the return for a bet on a given horse

(predicting a particular class) through changing the

Prevalences and thus EvennessR and the Odds. The

overall return can thus increase irrespective of the

success of bets in relation to those new wins. In

practice, we normally assume that we are making our

predictions on the basis of fixed (but not necessarily

known) Prevalences which may be estimated a priori

(from past data) or post hoc (from the experimental

data itself), and for our purposes are assumed to be

estimated from the contingency table.

Generalization of Significance

In relation to Significance, the single class χ+P2 and

G+P2 definitions both can be formulated in terms of

cell counts and a function of ratios, and would

normally be summed over at least (K−1)2 cells of a K-

class contingency table with (K−1)2 degrees of

freedom to produce a statistic for the table as a

whole. However, these statistics are not independent

of which variables are selected for evaluation or

summation, and the p-values obtained are thus quite

misleading, and for highly skewed distributions (in

terms of Bias or Prevalence) can be outlandishly

incorrect. If we sum log-likelihood (31) over all K2

cells we get N·MI(R||P) which is invariant over

Inverses and Duals.

The analogous Prevalence-weighted multi-class

statistic generalized from the Bookmaker

Informedness form of the Significance statistic, and

the Bias-weighted statistic generalized from the

Markedness form, extend Eqns 32-34 to the K>2

case by probability-weighted summation (this is a

weighted Arithmetic Mean of the individual cases

targeted to r=K-1 degree of freedom):

χ2KB = KN·B2·EvennessR– (47)

χ2KM = KN·M2·EvennessP– (48)

χ2KBM= KN·B·M·EvennessG– (49)

For K=2 and r=1, the Evenness terms were the

product of two complementary Prevalence or Bias

terms in both the Bookmaker derivations and the

Significance Derivations, and (30) derived a single

multiplicative Evenness factor from a squared

Evenness factor in the numerator deriving from

dtp2, and a single Evenness factor in the

denominator. We will discuss both these Evenness

terms in the a later section. We have marked the

Evenness terms in (47-49) with a trailing minus to

distinguish them from forms used in (20-22,44-46).

One specific issue with the goodness-of-fit approach

applied to K-class contingency tables relates to the

up to (K−1)2 degrees of freedom, which we focus on

now. The assumption of independence of the counts

in (K−1)2 of the cells is appropriate for testing the null

hypothesis, H0, and the calculation versus alpha,

but is patently not the case when the cells are

generated by K condition variables and K prediction

variables that mirror them. Thus a correction is in

order for the calculation of beta for some specific

alternate hypothesis HA or to examine the significance

of the difference between two specific hypotheses HA

and HB which may have some lesser degree of

difference.

Whilst many corrections are possible, in this case

correcting the degrees of freedom directly seems

appropriate and whilst using r = (K−1)2 degrees of

freedom is appropriate for alpha, using r = K−1

degrees of freedom is suggested for beta under the

conditions where significance is worth testing, given

the association (mirroring) between the variables is

almost complete. In testing against beta, as a

threshold on the probability that a specific alternate

hypothesis of the tested association being valid

should be rejected. The difference in a χ2 statistic

between two systems (r = K−1) can thus be tested

for significance as part of comparing two systems (the

Correlation-based statistics are recommended in this

case). The approach can also compare a system

against a model with specified Informedness (or

Markedness). Two special cases are relevant here,

H0, the null hypothesis corresponding to null

Informedness (B = 0: testing alpha with r =

(K−1)2), and H1, the full hypothesis corresponding to

full Informedness (B = 1: testing beta with r =

K−1).

Equations 47-49 are proposed for interpretation under

r = K−1 degrees of freedom (plus noise) and are

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hypothesized to be more accurate for investigating

the probability of the alternate hypothesis in question,

HA (beta).

Equations 50-52 are derived by summing over the

(K−1) complements of each class and label before

applying the Prevalence or bias weighted sum across

all predictions and conditions. These measures are

thus applicable for interpretation under r = (K−1)2

degrees of freedom (plus biases) and are

theoretically more accurate for estimating the

probability of the null hypothesis H0 (alpha). In

practice, the difference should always be slight (as

the cumulative density function of the gamma

distribution χ2 is locally near linear in r – see Fig. 4)

reflecting the usual assumption that alpha and

beta may be calculated from the same distribution.

Note that there is no difference in either the formulae

nor r when K=2.

χ2XB = K(K−1)·N·B2·EvennessR– (50)

χ2XBM = K(K−1)·N·B·M·EvennessG– (52)

Equations 53-55 are applicable to naïve unweighted

summation over the entire contingency table, but also

correspond to the independence test with r = (K−1)2

degrees of freedom, as well as slightly

underestimating but asymptotically approximating the

case where Evenness is maximum in (50-52) at

1/K2. When the contingency table is uneven,

Evenness factors will be lower and a more

conservative p-value will result from (50-52), whilst

summing naively across all cells (53-55) they can

lead to inflated statistics and underestimated p-

values. However, they are the equations that

correspond to common usage of the χ2 and G2

statistics as well as giving rise implicitly to Cramer’s V

= [χ2/N(K-1)]1/2 as the corresponding estimate of

the Pearson correlation coefficient, ρ, so that

Cramer’s V is thus also likely to be inflated as an

estimate of association where Evenness is low. We

however, note these, consistent with the usual

conventions, as our definitions of the conventional

forms of the χ2 statistics applied to the multiclass

generalizations of the Bookmaker

accuracy/association measures:

χ2B = (K−1)·N·B2 (53)

χ2M = (K−1)·N·M2 (54)

χ2BM = (K−1)·N·B·M (55)

Note that Cramer’s V calculated from standard full

Figure 4. Chi-squared against degrees of freedom cumulative density isocontours

(relative to α = 0.05: cyan/yellow boundary of p/α=1=1E0)

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56 Journal of Machine Learning Technology

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contingency χ2 and G2 estimates tends vastly

overestimate the level of association as measured by

Bookmaker and Markedness or constructed

empirically. It is also important to note that the full

matrix significance estimates (and hence Cramer’s V

and similar estimates from these χ2 statistics) are

independent of the permutations of predicted labels

(or real classes) assigned to the contingency tables,

and that in order to give such an independent

estimate using the above family of Bookmaker

statistics, it is essential that the optimal assignment of

labels is made – perverse solutions with suboptimal

allocations of labels will underestimate the

significance of the contingency table as they clearly

do take into account what one is trying to

demonstrate and how well we are achieving that goal.

The empirical observation concerning Cramer’s V

suggests that the strict probabilistic interpretation of

the multiclass generalized Informedness and

Markedness measures (probability of an informed or

marked decision), is not reflected by the traditional

correlation measures, the squared correlation being a

coefficient of proportionate determination of variance

and that outside of the 2D case where they match up

with BMG, we do not know how to interpret them as a

probability. However, we also note that Informedness

and Markedness tend to correlate and are at most

conditionally independent (given any one cell, e.g

given tp), so that their product cannot necessarily be

interpreted as a joint probability (they are

conditionally dependent given a margin, viz.

prevalence rp or bias pp: specifying one of B or M

now constrains the other; setting bias=prevalence, as

a common heuristic learning constraint, maximizes

correlation at BMG=B=M).

We note further that we have not considered a

tetrachoric correlation, which estimates the

regression of assumed underlying continuous

variables to allow calculation of their Pearson

Correlation.

Sketch Proof of General Chi-squared Test

The traditional χ2 statistic sums over a number of

terms specified by r degrees of freedom, stopping

once dependency emerges. The G2 statistic derives

from a log-likelihood analysis which is also

approximated, but less reliably, by the χ2 statistic. In

both cases, the variates are assumed to be

asymptotically normal and are expected to be

normalized to mean µ=0, standard deviation σ=1, and

both the Pearson and Matthews correlation and the

χ2 and G

2 significance statistics implicitly perform

such a normalization. However, this leads to

significance statistics that vary according to which

term is in focus if we sum over r rather than K2. In

the binary dichotomous case, it makes sense to sum

over only the condition of primary focus, but in the

general case it involves leaving out one case (label

and class). By the Central Limit Theorem, summing

over (K-1)2 such independent z-scores gives us a

normal distribution with σ=(K-1).

We define a single case χ2+lP from the χ2+P (30)

calculated for label l = class c as the positive

dichotomous case. We next sum over these for all

labels other than our target c to get a (K-1)2 degree

of freedom estimate χ2-lXP given by

χ2-lXP = ∑c≠l χ2+lP= ∑c χ2+cP – χ2+lP (56)

We then perform a Bias(l) weighted sum over χ2-lXP

to achieve our label independent (K-1)2 degree of

freedom estimate χ2XB as follows (substituting from

equation 30 then 39):

χ2XB =∑lBias(l) · [N·B2·EvennessR(l)/Bias(l) – χ2+lP]

=K · χ2KB – χ2KB= (K-1) · χ2KB

=K(K-1) ·N·B2·EvennessR (57)

This proves the Informedness form of the generalized

(K-1)2 degree of freedom χ2 statistic (42), and

defines EvennessR as the Arithmetic Mean of the

individual dichotomous EvennessR(l) terms

(assuming B is constant). The Markedness form of

the statistic (43) follows by analogous (Dual)

argument, and the Correlation form (44) is simply the

Geometric Mean of these two forms. Note however

that this proof assumes that B is constant across all

labels, and that assuming the determinant det is

constant leads to a derivative of (20-21) involving a

Harmonic Mean of Evenness as discussed in the next

section.

The simplified (K-1) degree of freedom χ2K statistics

were motivated as weighted averages of the

dichotomous statistics, but can also be seen to

approximate the χ2X statistics given the observation

that for a rejection threshold on the null hypothesis

H0, alpha< 0.05, the χ2 cumulative isodensity lines

are locally linear in r (Fig. 4). Testing differences

within a beta threshold as discussed above, is

appropriate using the χ2K series of statistics since

they are postulated to have (K-1) degrees of

freedom. Alternately they may be tested according to

the χ2X series of statistics given they are postulated to

differ in (K-1)2 degrees of freedom, namely the

noise, artefact and error terms that make the cells

different between the two hypotheses (viz. that

contribute to decorrelation). In practice, when used to

test two systems or models other than the null, the

models should be in a sufficiently linear part of the

isodensity contour to be insensitive to the choice of

statistic and the assumptions about degrees of

freedom. When tested against the null model, a

relatively constant error term can be expected to be

introduced by using the lower degree of freedom

model. The error introduced by the Cramer’s V (K-1

degree of freedom) approximation to significance

from G2 or χ2 can be viewed in two ways. If we start

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with a G2 or χ2 estimate as intended by Cramer we

can test the accuracy of the estimate versus the true

correlation, markedness and informedness as

illustrated in Fig. 5. Note that we can see here that

Cramer’s V underestimates association for high levels

of informedness, whilst it is reasonably accurate for

lower levels. If we use (53) to (55) to estimate

significance from the empirical association measures,

we will thus underestimate significance under

conditions of high association – viz. it the test is more

conservative as the magnitude of the effect

increases.

Generalization of Evenness

The proof that the product of dichotomous Evenness

factors is the appropriate generalization in relation to

the multiclass definition of Bookmaker Informedness

and Markedness does not imply that it is an

appropriate generalization of the dichotomous usage

of Evenness in relation to Significance, and we have

seen that the Arithmetic rather the Geometric Mean

emerged in the above sketch proof. Whilst in general

one would assume that Arithmetic and Harmonic

Means approximate the Geometric Mean, we argue

that the latter is the more appropriate basis, and

indeed one may note that it not only approximates the

Geometric Mean of the other two means, but is much

more stable as the Arithmetic and Harmonic means

can diverge radically from it in very uneven situations,

and increasingly with higher dimensionality. On the

other hand, the Arithmetic Mean is insensitive to

evenness and is thus appropriate as a baseline in

determining evenness. Thus the ratios between the

means, as well as between the Geometric Mean and

the geometric mean of the Arithmetic and Harmonic

means, give rise to good measures of evenness.

On geometric grounds we introduced the Determinant

of Correlation, det, generalizing dp, and

representing the volume of possible deviations from

chance covered by the target system and its

perversions, showing its normalization to and

Informedness-like statistic is EvennessP+ the product

of the Prevalences (and is exactly Informedness for

K=2). This gives rise to an alternative dichotomous

formulation for the aggregate false positive error for

an individual case in terms of the K-1 negative

cases, using a ratio or submatrix determinant to

submatrix product of Prevalences. This can be

extended to all K cases while reflecting K-1 degrees

of freedom, by extending to the full contingency

matrix determinant, det, and the full product of

Prevalences, as our definition of another form of

Evenness, EvennessR# being the Harmonic Mean of

the dichotomous Evenness terms for constant

determinant:

χ2KB = KN·det2/K / EvennessR# (58)

χ2KM = KN·det2/K / EvennessP# (59)

χ2KBM = KN·det2/K / EvennessG# (60)

Recall that the + form of Evenness is exemplified by

EvennessR+ = [ΠlPrev(l)]2/K =PrevG (61)

Figure 5. Illustration of significance and Cramer’s V.

110Monte Carlo simulations with 11 stepped expected

Informedness (red) levels with Bookmaker-

estimated Informedness (red dots), Markedness (green

dot) and Correlation (blue dot), with significance (p+1)

calculated using G2, X2, and Fisher estimates, and

(skewed) Cramer’s V Correlation estimates calculated

from both G2 and X2. Here K=4, N=128, X=1.96,

α=β=0.05.

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

58 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

and that the relationship between the three forms of

Evenness is of the form

EvennessR– = EvennessR+/ EvennessR# (62)

where the + form is defined as the squared Geometric

Mean (44-46), again suggesting that the – form is

best approximated as an Arithmetic Mean (47-49).

The above division by the Harmonic Mean is

reminiscent of the Williams’ correction which divides

the G2 values by an Evenness-like term q=1+(a2-

1)/6Nr where a is the number of categories for a

goodness-of-fit test, K [18-20] or more generally,

K/PrevH [17] which has maximum K when

Prevalence is even, and r=K-1 degrees of freedom,

but for the more relevant usage as an independence

test on a complete contingency table with r=(K-

1)2 degrees of freedom it is given by a2-

1=(K/PrevH-1)·(K/BiasH-1) where PrevH

and BiasH are the Harmonic Means across the K

classes or labels respectively [17-23].

In practice, any reasonable excursion from Evenness

will be reflected adequately by any of the means

discussed, however it is important to recognize that

the + form is actually a squared Geometric Mean and

is the product of the other two forms as shown in (62).

An uneven bias or Prevalence will reduce all the

corresponding Evenness forms, and compensate

against reduced measures of association and

significance due to lowered determinants.

Whereas broad assumptions and gross accuracy

within an order of magnitude may be acceptable for

calculating significance tests and p-values [23], it is

clearly not appropriate for estimate the strength of

associations. Thus the basic idea of Cramer’s V is

flawed given the rough assumptions and substantial

errors associated with significance tests. It is thus

better to start with a good measure of association,

and use analogous formulae to estimate significance

or confidence.

Generalization of Confidence

The discussion of confidence generalizes directly to

the general case, with the approximation using

Bookmaker Informedness1, or analogously

Markedness, applying directly (the Informedness form

is again a Prevalence weighted sum, in this case of a

sum of squared versus absolute errors), viz.

CIB2= X · [1-|B|] / √[2 E· (N-1)] (63)

CIM2= X · [1-|B|] / √[2 E· (N-1)] (64)

CIC2= X · [1-|B|] / √[ 2 E· (N-1)] (65)

1 Informedness may be dichotomous and relates in this

form to DeltaP, WRacc and the Gini Coefficient as

discussed below. Bookmaker Informedness refers to the

polychotomous generalization based on the Bookmaker

analogy and algorithm [4].

In Equations 63-65 Confidence Intervals derived from

the sse estimates of §2.8 are subscripted to show

those appropriate to the different measures of

association (Bookmaker Informedness, B;

Markedness, M, and their geometric mean as a

symmetric measure of Correlation, C). Those shown

relate to beta (the empirical hypothesis based on

the calculated B, giving rise to a test of power), but

are also appropriate both for significance testing the

null hypothesis (B=0) and provide tight (0-width)

bounds on the full correlation (B=1) hypothesis as

appropriate to its signification of an absence of

random variation and hence 100% power (and

extending this to include measurement error,

discretization error, etc.)

The numeric subscript is 2 as notwithstanding the

different assumptions behind the calculation of the

confidence intervals (0 for the null hypothesis

corresponding to alpha=0.05, 1 for the alternate

hypothesis corresponding to beta=0.05 based on

the weighted arithmetic model, and 2 for the full

correlation hypothesis corresponding to

gamma=0.05 – for practical purposes it is reasonable

to use |1-B| to define the basic confidence interval

for CIB0, CIB1 and CIB2, given variation is due solely

to unknown factors other than measurement and

discretization error. Note that all error, of whatsoever

kind, will lead to empirical estimates B<1.

If the empirical (CIB1) confidence intervals include

B=1, the broad confidence intervals (CIB2) around a

theoretical expectation of B=1 would also include the

empirical contingency – it is a matter of judgement

based on an understanding of contributing error

whether the hypothesis B=1 is supported given non-

zero error. In general B=1 should be achieved

empirically for a true correlation unless there are

measurement or labelling errors that are excluded

from the informedness model, since B<1 is always

significantly different from B=1 by definition (1-B=0

unaccounted variance due to guessing).

None of the traditional confidence or significance

measures fully account for discretization error (N<8K)

or for the distribution of margins, which are ignored by

traditional approaches. To deal with discretization

error we can adopt an sse estimate that is either

constant independent of B, such as the unweighted

arithmetic mean, or a non-trivial function that is non-

zero at both B=0 and B=1, such as the weighted

arithmetic mean which leads to:

CIB1= X ·[1-2|B|+2B2] / √[2 E· (N-1)] (66)

CIM1= X · [1-2|B|+2B2] / √[2 E· (N-1)] (67)

CIC1= X ·[1-2|B|+2B2] / √[ 2 E· (N-1)] (68)

Substituting B=0 and B=1 into this gives equivalent

CIs for the null and full hypothesis. In fact it is

sufficient to use the B=0 and 1 confidence intervals

based on this variant since for X=2 they overlap at

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Copyright © 2011 Bioinfo Publications 59

N<16. We illustrate such a marginal significance case

in Fig. 6, where the large difference between the

significance estimates is clear with Fisher showing

marginal significance or better almost everywhere, G2

for B>~0.6, χ2 for B>~0.8. >~95% of Bookmaker

estimates are within the confidence bands as

required (with 100% bounded by the more

conservative lower band), however our B=0 and B=1

confidence intervals almost meet showing that we

cannot distinguish intermediate B values other than

B=0.5 which is marginal. Viz. we can say that this

data seems to be random (B<0.5) or informed

(B>0.5), but cannot be specific about the level of

informedness for this small N (except for

B=0.5±0.25).

If there is a mismatch of the marginal weights

between the respective prevalences and biases, this

is taken to contravene our assumption that

Bookmaker statistics are calculated for the optimal

assignment of class labels. Thus we assume that

any mismatch is one of evenness only, and thus we

set the Evenness factor E=PrevG*BiasG*K2. Note

that the difference between Informedness and

Markedness also relates to Evenness, but

Markedness values are likely to lie outside bounds

attached to Informedness with probability greater than

the specified beta. Our model can thus take into

account distribution of margins provided the optimal

allocation of predictions to categories (labelling) is

assigned.

The multiplier X shown is set from the appropriate

(inverse cumulative) Normal or Poisson distribution,

and under the two-tailed form of the hypothesis,

X=1.96 gives alpha, beta and gamma of 0.05.

A multiplier of X=1.65 is appropriate for a one-tailed

hypotheses at 0.05 level. Significance of difference

from another model is satisfied to the specified level if

the specified model (including null or full) does not lie

in the confidence interval of the alternate model.

Power is adequate to the specified level if the

alternate model does not lie in the confidence interval

of the specified model. Figure 7 further illustrates the

effectiveness of the 95% empirical and theoretical

confidence bounds in relation to the significance

achievable at N=128 (K=5).

EXPLORATION AND FUTURE WORK

Powers Bookmaker Informedness has been used

extensively by proponent and his students over the

last 10 years, in particular in the PhD Theses and

other publications relating to AudioVisual Speech

Recognition [25-26] and EEG/Brain Computer

Interface [27-28], plus Matlab scripts that are

available for calculating both the standard and

Bookmaker statistics2 (these were modified by the

present author to produce the results presented in

this paper). The connection with DeltaP was noted in

the course of collaborative research in

Psycholinguistics, and provides an important

2 http://www.mathworks.com/matlabcentral/fileexchange/

5648-informedness-of-a-contingency-matrix

Figure 6. Illustration of significance and confidence.

110 Monte Carlo simulations with 11 stepped

expected Informedness levels (red line) with

Bookmaker-estimated Informedness (red dots),

Markedness (green dot) and Correlation (blue dot),

with significance (p+1) calculated using G2, X2, and

Fisher estimates, and confidence bands shown for

both the theoretical Informedness and the B=0 and

B=1 levels (parallel almost meeting at B=0.5). The

lower theoretical band is calculated twice, using both

CIB1andCIB2. Here K=4, N=16, X=1.96, α=β=0.05.

Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation

60 Journal of Machine Learning Technology

ISSN: 2229-3981 & ISSN: 2229-399X, Volume 2, Issue 1, 2011

Psychological justification or confirmation of the

measure where biological plausibility is desired. We

have referred extensively to the equivalence of

Bookmaker Informedness to ROC AUC, as used

standardly in Medicine, although AUC has the

apparent form of an undemeaned probability based

on a parameterized classifier or a series of classifiers,

and B is a demeaned renormalized kappa-like form

based on a single fully specified classifier.

The Informedness measure has thus proven its worth

across a wide range of disciplines, at least in its

dichotomous form. A particular feature of the major

PhD studies that used Informedness, is that they

covered different numbers of classes (exercising the

multi-class form of Bookmaker as implemented in

Matlab), as well as a number of different noise and

artefact conditions. Both of these aspects of their

work meant that the traditional measures and

derivatives of Recall, Precision and Accuracy were

useless for comparing the different runs and the

different conditions, whilst Bookmaker gave clear

unambiguous, easily interpretable results which were

contrasted with the traditional measures in these

studies.

The new χ2KB, χ2KM and χ2KBM,χ2XB, χ2XM and

χ2XBM correlation statistics were developed

heuristically with approximative sketch

proofs/arguments, and have only been investigated to

date in toy contrived situations and the Monte Carlo

simulations in Figs 2, 3, 5, 6 and 7. In particular,

whilst they work well in the dichotomous state, where

they demonstrate a clear advantage over χ2

traditional approaches, there has as yet been no no

application to our multi-class experiments and no

major body of work comparing new and conventional

approaches to significance. Just as Bookmaker (or

DeltaP') is the normative measure of accuracy for a

system against a Gold Standard, so is χ2XB the

proposed χ2 significance statistic for this most

common situation in the absence of a more specific

model (noting that ∑x = ∑x2 for dichotomous data in

{0,1}). For the cross-rater or cross-system

comparison, where neither is normative, the BMG

Correlation is the appropriate measure, and

correspondingly we propose that χ2KBM is the

appropriate χ2 significance statistic. To explore these

thoroughly is a matter for future research. However,

in practice we tend to recommend the use of

Confidence Intervals as illustrated in Figs 4 and 5,

since these give a direct indication of power versus

the confidence interval on the null hypothesis, as well

as power when used with confidence intervals on an

alternate hypothesis.

Furthermore, when used on the empirical mean

(correlation, markedness or informedness), the

overlap of the interval with another system, and vice-

versa, give direct indication of both significance and

power of the difference between them. If a system

occurs in another confidence interval it is not

significantly different from that system or hypothesis,

and if it is it is significantly different. If its own

confidence interval also avoids overlapping the

alternate mean this mutual significance is actually a

Figure 7. Illustration of significance and confidence.

110 Monte Carlo simulations with 11 stepped expected

Informedness levels (red line) with Bookmaker-

estimated Informedness (red dots), Markedness (green

dot) and Correlation (blue dot), with significance (p+1)

calculated using G2, X2, and Fisher estimates, and

confidence bands shown for both the theoretical

Informedness and the B=0 and B=1 levels (parallel

almost meeting at B=0.5). The lower theoretical band

is calculated twice, using both CIB1andCIB2. Here K=5,

N=128, X=1.96, ==0.05.

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reflection of statistical power at a complementary

level. However, as with significance tests, it is

important to avoid reading to avoid too much into

non-overlap of interval and mean (not of intervals) as

the actual probabilities of the hypotheses depends

also on unknown priors.

Thus whilst our understanding of Informedness and

Markedness as performance measure is now quite

mature, particularly in view of t