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explain_subset (list) - List of feature indices. If specified, only selects a subset of the features in the evaluation dataset for explanation. The subset can be the top-k features from the model summary. features (list) - A list of feature names. classes (list) - Class names as a list of strings. The order of the class names should match. Finally, within model-agnostic approaches, there are different methods, e.g. permutation and SHAP (Shapley Additive Explanations). So, to summarize, variable importance “methodologies” can be broken down in several ways: model-specific vs. model-agnostic approach; loss function 3 . model agnostic method (given a model agnostic approach). Pre-processing. Let's remember the logistic regression equation first. z = w 0 + w 1 x 1 + w 2 x 2 + w 3 x 3 + w 4 x 4. y = 1 / (1 + e-z). x1 stands for sepal length; x2 stands for sepal width; x3 stands for petal length; x4 stands for petal width.

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Permutation Feature Importance(PFI) Explainer based on Breiman's paper on Rander Forest, which works by shuffling data one feature at a time for the entire dataset and estimating how it affects the performance metric; the larger the change, the more important the feature is. It can explain the overall behavior and not the individual predictions.

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