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Distribution of the average model size and corresponding ranks per problem, broken down by noise level σ. The left value in each cell is the median, the right value the interquartile range. The right column are the values outlined in Figures 3 and 4.
Source publication
![For each problem and algorithm, ten models were trained in SRBench [8]....](publication/386240114/figure/fig3/AS:11431281363961820@1744176865022/For-each-problem-and-algorithm-ten-models-were-trained-in-SRBench-8-The-box-plots_Q320.jpg)
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Scientific machine learning, white box modelling.
Abstract
Symbolic regression is commonly used in domains where both high accuracy and interpretability of models is required. While symbolic regression is capable to produce highly accurate models, small changes in the training data might cause highly dissimilar solution. The i...
Contexts in source publication
Context 1
... median and the interquartile range of all error values and their corresponding ranks, broken down by noise level, are shown in Table 1. Table 2 shows the same statistics for the size of all models. The box of FFX in Figure 3b is cut off to prevent distortion of the axis, but the distribution is described in detail in Table 2. Table 1 shows that most algorithms perform similar across all noise levels. ...Context 2
... 2 shows the same statistics for the size of all models. The box of FFX in Figure 3b is cut off to prevent distortion of the axis, but the distribution is described in detail in Table 2. Table 1 shows that most algorithms perform similar across all noise levels. ...Context 3
... recent work by Neal et al. [4] describes that both bias and variance in neural networks can decrease even when the number of parameters in a model shrinks by adapting the structure of the neural network. Table 2 shows that the noise level affects the size of models very little, with more noise leading to slightly larger models. ...Similar publications
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