Issue 68

M. Matin et alii, Frattura ed Integrità Strutturale, 68 (2024) 357-370; DOI: 10.3221/IGF-ESIS.68.24

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Figure 8: The histogram and scatter plot for the SHAP values of the logarithmic value of fatigue lifetimes modeling with XGBoost using all of the samples as trained data for different variables: (a) the fretting force, (b) the corrosion time, (c) nano-particles, (d) the stress, (e) the lubrication, and (f) the heat treatment. To illustrate how the game theory, in conjunction with SHAP values, can provide an overview for estimating the logarithm value of fatigue lifetimes, Fig. 9 showcases samples numbered 20 and 65 from the experimental dataset. In this figure, each feature is assigned a specified SHAP value, where   f x represents the estimated logarithm value of fatigue lifetimes and   E f x     denotes the predicted logarithm of fatigue lifetimes irrespective of features. Notably,   E f x     remains constant across all samples, equivalent to  0 y in Eqn. (3). The collected SHAP values for each specified sample are

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