X_train,X_test,Y_train,Y_test=train_test_split(X_data,Y_data,test_size=0.3,random_state=0) Train your model. # Train a RandomForest Modelmodel=RandomForestClassifier()model.fit(X_train,Y_train) After you're done training the model, you can either access the complete explainability dash...
privacy import DPExplainableBoostingClassifier, DPExplainableBoostingRegressor dp_ebm = DPExplainableBoostingClassifier(epsilon=1, delta=1e-5) # Specify privacy parameters dp_ebm.fit(X_train, y_train) show(dp_ebm.explain_global()) # Identical function calls to standard EBMs For more information, ...
Random Forest Random Forest is an ensemble technique, meaning that it combines several models into one to improve its predictive power. Specifically, it builds 1000s of smaller decision trees using bootstrapped datasets and random subsets of variables (also known as bagging). With 1000s of smaller...
For the RF and the DT, the sum of the insular cortices was selected, the XGBoost classifier chose the ratio of the inferior parietal lobule, the polynomial SVM selected the sum of the lingual gyri, the SVM with the radial kernel chose the sum of the temporal pole volumes and the LR ...
frominterpret.glassboximportExplainableBoostingClassifierebm=ExplainableBoostingClassifier()ebm.fit(X_train,y_train)# or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ...# EBM supports pandas dataframes, numpy arrays, and handles "string" data natively. ...
fit(X_train, y_train) # or substitute with LogisticRegression, DecisionTreeClassifier, RuleListClassifier, ... # EBM supports pandas dataframes, numpy arrays, and handles "string" data natively. Understand the model from interpret import show ebm_global = ebm.explain_global() show(ebm_global)...