Fig 1 - Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation

Graphic representation of supervised machine learning. In supervised learning, original preprocessed data sets, containing known variables and targets, are divided into training data and test data. (Above) During the training phase, the training data are used to train a learning algorithm in an attempt to develop an accurate predictive model. (Center) To validate the model, the test data are then applied to the model and predictive accuracy is assessed. (Below) Once validated, new data are input into the model in an attempt to make new predictions.

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Reference

Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation - Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Graphic-representation-of-supervised-machine-learning-In-supervised-learning-original_fig1_301688300 [accessed 27 Mar 2025]

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Fig. 1. Graphic representation of supervised machine learning. In supervised learning, original preprocessed data sets, containing known variables and targets, are divided into training data and test data. (Above) During the training phase, the training data are used to train a learning algorithm in an attempt to develop an accurate predictive model. (Center) To validate the model, the test data are then applied to the model and predictive accuracy is assessed. (Below) Once validated, new data are input into the model in an attempt to make new predictions.

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<a href="https://www.researchgate.net/figure/Graphic-representation-of-supervised-machine-learning-In-supervised-learning-original_fig1_301688300"><img src="https://www.researchgate.net/profile/Jonathan-Kanevsky/publication/301688300/figure/fig1/AS:677399805042688@1538516107353/Graphic-representation-of-supervised-machine-learning-In-supervised-learning-original.png" alt="Graphic representation of supervised machine learning. In supervised learning, original preprocessed data sets, containing known variables and targets, are divided into training data and test data. (Above) During the training phase, the training data are used to train a learning algorithm in an attempt to develop an accurate predictive model. (Center) To validate the model, the test data are then applied to the model and predictive accuracy is assessed. (Below) Once validated, new data are input into the model in an attempt to make new predictions."/></a>