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SHAPIQ

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    How to build an interpretable AI analysis pipeline using SHAP-IQ to understand feature importance, interaction effects, and model decision breakdown

    by March 2, 2026
    March 2, 2026

    INSTANCE_I = int(np.clip(INSTANCE_I, 0, len(X_test)-1)) x = X_test.iloc(INSTANCE_I).values y_true = float(y_test.iloc(INSTANCE_I)) pred = float(model.predict((x))(0)) iv = explainer.explain(x, budget=int(BUDGET_LOCAL), random_state=0) baseline = float(getattr(iv, “baseline_value”, 0.0)) main_effects = extract_main_effects(iv, feature_names) pair_df = …

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Recent Posts

  • SAM3 for Video: Context-Aware Segmentation and Object Tracking

    March 2, 2026
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    March 2, 2026
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    March 2, 2026
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