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Plots of single instances representing the importance and contribution of features in the learning of the CatBoost regressor model to predict the value of the self-rated oral sensitivity in a control individual (a) and in a patient with chemosensory loss (b). The Y axis represents the order of the features’ importance, while the X axis represents the contribution of features. Below the X axis, (E[f(x)]) is the average of the predicted values of the dataset which is the starting point to determine the contribution of each feature. At the top of the plot, f(x) is the predicted value in the single instance. The numbers next to each bar represent each feature’s contribution values.

Plots of single instances representing the importance and contribution of features in the learning of the CatBoost regressor model to predict the value of the self-rated oral sensitivity in a control individual (a) and in a patient with chemosensory loss (b). The Y axis represents the order of the features’ importance, while the X axis represents the contribution of features. Below the X axis, (E[f(x)]) is the average of the predicted values of the dataset which is the starting point to determine the contribution of each feature. At the top of the plot, f(x) is the predicted value in the single instance. The numbers next to each bar represent each feature’s contribution values.

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The gustatory, olfactory, and trigeminal systems are anatomically separated. However, they interact cognitively to give rise to oral perception, which can significantly affect health and quality of life. We built a Supervised Learning (SL) regression model that, exploiting participants’ features, was capable of automatically analyzing with high pre...

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