frominterpret.glassboximportExplainableBoostingClassifierebm=ExplainableBoostingClassifier()ebm.fit(X_train,y_train)# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...# EBM
SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this functionality is as simple as pass...
numpy>=1.15.2 pandas>=0.19.2 matplotlib>=3.1.3 scikit-learn>=0.23.0 pip install gaminet To use it on GPU, conda install tensorflow==2.2, pip install tensorflow-lattice==2.0.8, conda install tensorflow-estimators==2.2 Usage Import library ...
SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this functionality is as simple as pass...
SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this functionality is as simple as pass...
# ...include code from https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.pyimportshapimportnumpyasnp# select a set of background examples to take an expectation overbackground=x_train[np.random.choice(x_train.shape[0],100,replace=False)]# explain predictions of the model...
SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this functionality is as simple as pass...
SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this functionality is as simple as pass...
move back to 3.6 for testing because of numba numpy incompat Sep 23, 2021 Repository files navigation README MIT license SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanatio...
SHAP has specific support for natural language models like those in the Hugging Face transformers library. By adding coalitional rules to traditional Shapley values we can form games that explain large modern NLP model using very few function evaluations. Using this functionality is as simple as pass...