![]() ![]() var_selection_by_permute_cv: Perform Variable Selection Using Cross-validation Procedure.var_selection_by_permute: Perform Variable Selection using Three Threshold-based.summary.bartMachine: Summarizes information about a 'bartMachine' object.set_bart_machine_num_cores: Set the Number of Cores for BART.rmse_by_num_trees: Assess the Out-of-sample RMSE by Number of Trees.print.bartMachine: Summarizes information about a 'bartMachine' object.predict_bartMachineArr: Make a prediction on data using a BART array object.predict.bartMachine: Make a prediction on data using a BART object.plot_y_vs_yhat: Plot the fitted Versus Actual Response.plot_convergence_diagnostics: Plot Convergence Diagnostics.node_prediction_training_data_indices: Gets node predictions indices of the training data for new.k_fold_cv: Estimate Out-of-sample Error with K-fold Cross validation.investigate_var_importance: Explore Variable Inclusion Proportions in BART Model.interaction_investigator: Explore Pairwise Interactions in BART Model.get_var_props_over_chain: Get the Variable Inclusion Proportions.get_var_counts_over_chain: Get the Variable Inclusion Counts.get_sigsqs: Get Posterior Error Variance Estimates.get_projection_weights: Gets Training Sample Projection / Weights.extract_raw_node_data: Gets Raw Node data.destroy_bart_machine: Destroy BART Model (deprecated - do not use!).cov_importance_test: Importance Test for Covariate(s) of Interest.check_bart_error_assumptions: Check BART Error Assumptions.calc_prediction_intervals: Calculate Prediction Intervals.calc_credible_intervals: Calculate Credible Intervals.bart_predict_for_test_data: Predict for Test Data with Known Outcomes.bart_machine_num_cores: Get Number of Cores Used by BART.bart_machine_get_posterior: Get Full Posterior Distribution.bartMachineArr: Create an array of BART models for the same data.automobile: Data concerning automobile prices. ![]()
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