逻辑回归的沙普利？

Question

Does shapley support logistic regression models?

Running the following code i get:

logmodel = LogisticRegression()
logmodel.fit(X_train,y_train)
predictions = logmodel.predict(X_test)
explainer = shap.TreeExplainer(logmodel )

Exception: Model type not yet supported by TreeExplainer: <class 'sklearn.linear_model.logistic.LogisticRegression'>

PS You are supposed to use a different explainder for different models

Answer 1

Shap is model agnostic by definition. It looks like you have just chosen an explainer that doesn't suit your model type. I suggest looking at KernelExplainer which as described by the creators here is

An implementation of Kernel SHAP, a model agnostic method to estimate SHAP values for any model. Because it makes not assumptions about the model type, KernelExplainer is slower than the other model type specific algorithms.

The documentation for Shap is mostly solid and has some decent examples.

Answer 2

explainer = shap.LinearExplainer(logmodel)应该工作，因为逻辑回归是一个线性模型。

Answer 3

Logistic 回归是一个线性模型，因此您应该使用线性解释器。