interpret-core

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from interpret.glassbox import ExplainableBoostingClassifier from interpret import show # Generate some synthetic data np.random.seed(0) X = pd.DataFrame({ 'feature_a': np.random.rand(100) * 10, 'feature_b': np.random.randint(0, 3, 100).astype(str), 'feature_c': np.random.randn(100) }) y = (X['feature_a'] + (X['feature_b'].astype(int) * 2) + np.random.randn(100) > 10).astype(int) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Initialize and fit an Explainable Boosting Machine (EBM) ebm = ExplainableBoostingClassifier(random_state=42) ebm.fit(X_train, y_train) # Get global explanations (feature importances and shapes) ebm_global = ebm.explain_global() # In a notebook environment, you would call show(ebm_global) to visualize # For script execution, we can print summary or a representation of the explanation print(f"Global Explanation for EBM:\n{ebm_global.data()}") # Get local explanations for a specific sample ebm_local = ebm.explain_local(X_test[:1], y_test[:1]) print(f"\nLocal Explanation for first test sample:\n{ebm_local.data()}")