smile.feature.importance.package-info Maven / Gradle / Ivy
/*
* Copyright (c) 2010-2021 Haifeng Li. All rights reserved.
*
* Smile is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Smile is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with Smile. If not, see
* Global explanations tries to describe the model as whole, in terms of
* which variables/features influenced the general model the most. Two
* common methods for such an overall explanation is some kind of
* permutation feature importance or partial dependence plots.
* Permutation feature importance measures the increase in the prediction
* error of the model after permuting the feature's values, which breaks
* the relationship between the feature and the true outcome.
* The partial dependence plot (PDP) shows the marginal effect one or
* two features have on the predicted outcome of a machine learning model.
* A partial dependence plot can show whether the relationship between
* the target and a feature is linear, monotonic or more complex.
*
* Local explanations tries to identify how the different input
* variables/features influenced a specific prediction/output from the model,
* and are often referred to as individual prediction explanation methods.
* Such explanations are particularly useful for complex models which behave
* rather different for different feature combinations, meaning that the
* global explanation is not representative for the local behavior.
*
* Local explanation methods may further be divided into two categories:
* model-specific and model-agnostic (general) explanation methods.
* The model-agnostic methods usually try to explain individual predictions
* by learning simple, interpretable explanations of the model specifically
* for a given prediction. Three examples are Explanation Vectors, LIME,
* and Shapley values.
*
* @author Haifeng Li
*/
package smile.feature.importance;