| Acquire an IAI license for the current session. | acquire_license |
| Add additional Julia worker processes to parallelize workloads | add_julia_processes |
| Return a dataframe containing all treatment combinations of one or more treatment vectors, ready for use as treatment candidates in `fit_predict!` or `predict` | all_treatment_combinations |
| Return the leaf index in a tree model into which each point in the features falls | apply |
| Return the indices of the points in the features that fall into each node of a trained tree model | apply_nodes |
| Convert a vector of values to IAI mixed data format | as.mixeddata |
| Construct a 'ggplot2::ggplot' object plotting grid search results for Optimal Feature Selection learners | autoplot.grid_search |
| Construct a 'ggplot2::ggplot' object plotting the ROC curve | autoplot.roc_curve |
| Construct a 'ggplot2::ggplot' object plotting the results of the similarity comparison | autoplot.similarity_comparison |
| Construct a 'ggplot2::ggplot' object plotting the results of the stability analysis | autoplot.stability_analysis |
| Learner for conducting reward estimation with categorical treatments and classification outcomes | categorical_classification_reward_estimator |
| Learner for conducting reward estimation with categorical treatments and regression outcomes | categorical_regression_reward_estimator |
| Learner for conducting reward estimation with categorical treatments | categorical_reward_estimator |
| Learner for conducting reward estimation with categorical treatments and survival outcomes | categorical_survival_reward_estimator |
| Remove all traces of automatic Julia/IAI installation | cleanup_installation |
| Return an unfitted copy of a learner with the same parameters | clone |
| Convert `treatments` from symbol/string format into numeric values. | convert_treatments_to_numeric |
| Copy the tree split structure from one learner into another and refit the models in each leaf of the tree using the supplied data | copy_splits_and_refit_leaves |
| Return a matrix where entry '(i, j)' is true if the 'i'th point in the features passes through the 'j'th node in a trained tree model. | decision_path |
| Delete a global rich output parameter | delete_rich_output_param |
| Learner that estimates equal propensity for all treatments. | equal_propensity_estimator |
| Generic function for fitting a learner. | fit |
| Fit an imputation learner with training features and create adaptive indicator features to encode the missing pattern | fit_and_expand |
| Fits a grid search to the training data with cross-validation | fit_cv |
| Generic function for fitting a reward estimator on features, treatments and returning predicted counterfactual rewards and scores of the internal estimators. | fit_predict |
| Fit a categorical reward estimator on features, treatments and outcomes and return predicted counterfactual rewards for each observation, under each treatment observed in the data, as well as the scores of the internal estimators. | fit_predict.categorical_reward_estimator |
| Fit a numeric reward estimator on features, treatments and outcomes and return predicted counterfactual rewards for each observation, under each treatment candidate, as well as the scores of the internal estimators. | fit_predict.numeric_reward_estimator |
| Fit an imputation model using the given features and impute the missing values in these features | fit_transform |
| Train a grid using cross-validation with features and impute all missing values in these features | fit_transform_cv |
| Fits a 'grid_search' to the training data | fit.grid_search |
| Fits an imputation learner to the training data. | fit.imputation_learner |
| Fits a model to the training data | fit.learner |
| Fits an Optimal Feature Selection learner to the training data | fit.optimal_feature_selection_learner |
| Return the best parameter combination from a grid | get_best_params |
| Generic function for returning the predicted label in the node of a classification tree | get_classification_label |
| Return the predicted label at a node of a tree | get_classification_label.classification_tree_learner |
| Return the predicted label at a node of a multi-task tree | get_classification_label.classification_tree_multi_learner |
| Generic function for returning the probabilities of class membership at a node of a classification tree | get_classification_proba |
| Return the predicted probabilities of class membership at a node of a tree | get_classification_proba.classification_tree_learner |
| Return the predicted probabilities of class membership at a node of a multi-task tree | get_classification_proba.classification_tree_multi_learner |
| Return the indices of the trees assigned to each cluster, under the clustering of a given number of trees | get_cluster_assignments |
| Return the centroid information for each cluster, under the clustering of a given number of trees | get_cluster_details |
| Return the distances between the centroids of each pair of clusters, under the clustering of a given number of trees | get_cluster_distances |
| Get the depth of a node of a tree | get_depth |
| Return the total kernel density surrounding each treatment candidate for the propensity/outcome estimation problems in a fitted learner. | get_estimation_densities |
| Return the names of the features used by the learner | get_features_used |
| Return a vector of lists detailing the results of the grid search | get_grid_result_details |
| Return a summary of the results from the grid search | get_grid_result_summary |
| Return a summary of the results from the grid search | get_grid_results |
| Return the fitted learner using the best parameter combination from a grid | get_learner |
| Get the index of the lower child at a split node of a tree | get_lower_child |
| Return the machine ID for the current computer. | get_machine_id |
| Generic function for returning the number of fits in a trained learner | get_num_fits |
| Return the number of fits along the path in a trained GLMNet learner | get_num_fits.glmnetcv_learner |
| Return the number of fits along the path in a trained Optimal Feature Selection learner | get_num_fits.optimal_feature_selection_learner |
| Return the number of nodes in a trained learner | get_num_nodes |
| Get the number of training points contained in a node of a tree | get_num_samples |
| Return the value of all parameters on a learner | get_params |
| Get the index of the parent node at a node of a tree | get_parent |
| Return the quality of the treatments at a node of a tree | get_policy_treatment_outcome |
| Return the standard error for the quality of the treatments at a node of a tree | get_policy_treatment_outcome_standard_error |
| Return the treatments ordered from most effective to least effective at a node of a tree | get_policy_treatment_rank |
| Generic function for returning the prediction constant in a trained learner | get_prediction_constant |
| Return the constant term in the prediction in a trained GLMNet learner | get_prediction_constant.glmnetcv_learner |
| Return the constant term in the prediction in a trained Optimal Feature Selection learner | get_prediction_constant.optimal_feature_selection_learner |
| Generic function for returning the prediction weights in a trained learner | get_prediction_weights |
| Return the weights for numeric and categoric features used for prediction in a trained GLMNet learner | get_prediction_weights.glmnetcv_learner |
| Return the weights for numeric and categoric features used for prediction in a trained Optimal Feature Selection learner | get_prediction_weights.optimal_feature_selection_learner |
| Return the treatments ordered from most effective to least effective at a node of a tree | get_prescription_treatment_rank |
| Generic function for returning the constant term in the regression prediction at a node of a tree | get_regression_constant |
| Return the constant term in the logistic regression prediction at a node of a classification tree | get_regression_constant.classification_tree_learner |
| Return the constant term in the logistic regression prediction at a node of a multi-task classification tree | get_regression_constant.classification_tree_multi_learner |
| Return the constant term in the linear regression prediction at a node of a prescription tree | get_regression_constant.prescription_tree_learner |
| Return the constant term in the linear regression prediction at a node of a regression tree | get_regression_constant.regression_tree_learner |
| Return the constant term in the linear regression prediction at a node of a multi-task regression tree | get_regression_constant.regression_tree_multi_learner |
| Return the constant term in the cox regression prediction at a node of a survival tree | get_regression_constant.survival_tree_learner |
| Generic function for returning the weights for each feature in the regression prediction at a node of a tree | get_regression_weights |
| Return the weights for each feature in the logistic regression prediction at a node of a classification tree | get_regression_weights.classification_tree_learner |
| Return the weights for each feature in the logistic regression prediction at a node of a multi-task classification tree | get_regression_weights.classification_tree_multi_learner |
| Return the weights for each feature in the linear regression prediction at a node of a prescription tree | get_regression_weights.prescription_tree_learner |
| Return the weights for each feature in the linear regression prediction at a node of a regression tree | get_regression_weights.regression_tree_learner |
| Return the weights for each feature in the linear regression prediction at a node of a multi-task regression tree | get_regression_weights.regression_tree_multi_learner |
| Return the weights for each feature in the cox regression prediction at a node of a survival tree | get_regression_weights.survival_tree_learner |
| Return the current global rich output parameter settings | get_rich_output_params |
| Extract the underlying data from an ROC curve | get_roc_curve_data |
| Return the categoric/ordinal information used in the split at a node of a tree | get_split_categories |
| Return the feature used in the split at a node of a tree | get_split_feature |
| Return the threshold used in the split at a node of a tree | get_split_threshold |
| Return the weights for numeric and categoric features used in the hyperplane split at a node of a tree | get_split_weights |
| Return the trained trees in order of increasing objective value, along with their variable importance scores for each feature | get_stability_results |
| Return the survival curve at a node of a tree | get_survival_curve |
| Extract the underlying data from a survival curve (as returned by 'predict.survival_learner' or 'get_survival_curve') | get_survival_curve_data |
| Return the predicted expected survival time at a node of a tree | get_survival_expected_time |
| Return the predicted hazard ratio at a node of a tree | get_survival_hazard |
| Extract the training objective value for each candidate tree in the comparison, where a lower value indicates a better solution | get_train_errors |
| Return a copy of the learner that uses a specific tree rather than the tree with the best training objective. | get_tree |
| Get the index of the upper child at a split node of a tree | get_upper_child |
| Learner for training GLMNet models for classification problems with cross-validation | glmnetcv_classifier |
| Learner for training GLMNet models for regression problems with cross-validation | glmnetcv_regressor |
| Learner for training GLMNet models for survival problems with cross-validation | glmnetcv_survival_learner |
| Controls grid search over parameter combinations | grid_search |
| Initialize Julia and the IAI package. | iai_setup |
| Generic learner for imputing missing values | imputation_learner |
| Impute missing values using either a specified method or through validation | impute |
| Impute missing values using cross validation | impute_cv |
| Download and install Julia automatically. | install_julia |
| Download and install the IAI system image automatically. | install_system_image |
| Check if a node of a tree applies a categoric split | is_categoric_split |
| Check if a node of a tree applies a hyperplane split | is_hyperplane_split |
| Check if a node of a tree is a leaf | is_leaf |
| Check if a node of a tree applies a mixed ordinal/categoric split | is_mixed_ordinal_split |
| Check if a node of a tree applies a mixed parallel/categoric split | is_mixed_parallel_split |
| Check if a node of a tree applies a ordinal split | is_ordinal_split |
| Check if a node of a tree applies a parallel split | is_parallel_split |
| Loads the Julia Graphviz library to permit certain visualizations. | load_graphviz |
| Learner for conducting mean imputation | mean_imputation_learner |
| Check if points with missing values go to the lower child at a split node of of a tree | missing_goes_lower |
| Generic function for constructing an interactive questionnaire with multiple learners | multi_questionnaire |
| Construct an interactive questionnaire from multiple specified learners | multi_questionnaire.default |
| Construct an interactive tree questionnaire using multiple learners from the results of a grid search | multi_questionnaire.grid_search |
| Generic function for constructing an interactive tree visualization of multiple tree learners | multi_tree_plot |
| Construct an interactive tree visualization of multiple tree learners as specified by questions | multi_tree_plot.default |
| Construct an interactive tree visualization of multiple tree learners from the results of a grid search | multi_tree_plot.grid_search |
| Learner for conducting reward estimation with numeric treatments and classification outcomes | numeric_classification_reward_estimator |
| Learner for conducting reward estimation with numeric treatments and regression outcomes | numeric_regression_reward_estimator |
| Learner for conducting reward estimation with numeric treatments | numeric_reward_estimator |
| Learner for conducting reward estimation with numeric treatments and survival outcomes | numeric_survival_reward_estimator |
| Learner for conducting optimal k-NN imputation | opt_knn_imputation_learner |
| Learner for conducting optimal SVM imputation | opt_svm_imputation_learner |
| Learner for conducting optimal tree-based imputation | opt_tree_imputation_learner |
| Learner for conducting Optimal Feature Selection on classification problems | optimal_feature_selection_classifier |
| Learner for conducting Optimal Feature Selection on regression problems | optimal_feature_selection_regressor |
| Learner for training Optimal Classification Trees | optimal_tree_classifier |
| Learner for training multi-task Optimal Classification Trees | optimal_tree_multi_classifier |
| Learner for training multi-task Optimal Regression Trees | optimal_tree_multi_regressor |
| Learner for training Optimal Policy Trees where the policy should aim to maximize outcomes | optimal_tree_policy_maximizer |
| Learner for training Optimal Policy Trees where the policy should aim to minimize outcomes | optimal_tree_policy_minimizer |
| Learner for training Optimal Prescriptive Trees where the prescriptions should aim to maximize outcomes | optimal_tree_prescription_maximizer |
| Learner for training Optimal Prescriptive Trees where the prescriptions should aim to minimize outcomes | optimal_tree_prescription_minimizer |
| Learner for training Optimal Regression Trees | optimal_tree_regressor |
| Learner for training Optimal Survival Trees | optimal_tree_survival_learner |
| Learner for training Optimal Survival Trees | optimal_tree_survivor |
| Plot a grid search results for Optimal Feature Selection learners | plot.grid_search |
| Plot an ROC curve | plot.roc_curve |
| Plot a similarity comparison | plot.similarity_comparison |
| Plot a stability analysis | plot.stability_analysis |
| Generic function for returning the predictions of a model | predict |
| Generic function for returning the expected survival time predicted by a model | predict_expected_survival_time |
| Return the expected survival time estimate made by a 'glmnetcv_survival_learner' for each point in the features. | predict_expected_survival_time.glmnetcv_survival_learner |
| Return the expected survival time estimate made by a survival curve (as returned by 'predict.survival_learner' or 'get_survival_curve') | predict_expected_survival_time.survival_curve |
| Return the expected survival time estimate made by a survival learner for each point in the features. | predict_expected_survival_time.survival_learner |
| Generic function for returning the hazard coefficient predicted by a model | predict_hazard |
| Return the fitted hazard coefficient estimate made by a 'glmnetcv_survival_learner' for each point in the features. | predict_hazard.glmnetcv_survival_learner |
| Return the fitted hazard coefficient estimate made by a survival learner for each point in the features. | predict_hazard.survival_learner |
| Generic function for returning the outcomes predicted by a model under each treatment | predict_outcomes |
| Return the predicted outcome for each treatment made by a policy learner for each point in the features | predict_outcomes.policy_learner |
| Return the predicted outcome for each treatment made by a prescription learner for each point in the features | predict_outcomes.prescription_learner |
| Generic function for returning the probabilities of class membership predicted by a model | predict_proba |
| Return the probabilities of class membership predicted by a classification learner for each point in the features | predict_proba.classification_learner |
| Return the probabilities of class membership predicted by a multi-task classification learner for each point in the features | predict_proba.classification_multi_learner |
| Return the probabilities of class membership predicted by a 'glmnetcv_classifier' learner for each point in the features | predict_proba.glmnetcv_classifier |
| Generic function for returning the counterfactual rewards estimated by a model under each treatment | predict_reward |
| Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data and predictions | predict_reward.categorical_reward_estimator |
| Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data and predictions | predict_reward.numeric_reward_estimator |
| Calculate SHAP values for all points in the features using the learner | predict_shap |
| Return the estimated quality of each treatment in the trained model of the learner for each point in the features | predict_treatment_outcome |
| Return the standard error for the estimated quality of each treatment in the trained model of the learner for each point in the features | predict_treatment_outcome_standard_error |
| Return the treatments in ranked order of effectiveness for each point in the features | predict_treatment_rank |
| Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data | predict.categorical_reward_estimator |
| Return the predictions made by a GLMNet learner for each point in the features | predict.glmnetcv_learner |
| Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data | predict.numeric_reward_estimator |
| Return the predictions made by an Optimal Feature Selection learner for each point in the features | predict.optimal_feature_selection_learner |
| Return the predictions made by a supervised learner for each point in the features | predict.supervised_learner |
| Return the predictions made by a multi-task supervised learner for each point in the features | predict.supervised_multi_learner |
| Return the predictions made by a survival learner for each point in the features | predict.survival_learner |
| Print the decision path through the learner for each sample in the features | print_path |
| Use the trained trees in a learner along with the supplied validation data to determine the best value for the `cp` parameter and then prune the trees according to this value | prune_trees |
| Generic function for constructing an interactive questionnaire | questionnaire |
| Specify an interactive questionnaire of an Optimal Feature Selection learner | questionnaire.optimal_feature_selection_learner |
| Specify an interactive questionnaire of a tree learner | questionnaire.tree_learner |
| Learner for conducting random imputation | rand_imputation_learner |
| Learner for training random forests for classification problems | random_forest_classifier |
| Learner for training random forests for regression problems | random_forest_regressor |
| Learner for training random forests for survival problems | random_forest_survival_learner |
| Read in a learner or grid saved in JSON format | read_json |
| Refit the models in the leaves of a trained learner using the supplied data | refit_leaves |
| Release any IAI license held by the current session. | release_license |
| Reset the predicted probability displayed to be that of the predicted label when visualizing a learner | reset_display_label |
| Resume training from a checkpoint file | resume_from_checkpoint |
| Learner for conducting reward estimation with categorical treatments | reward_estimator |
| Generic function for constructing an ROC curve | roc_curve |
| Construct an ROC curve using a trained classification learner on the given data | roc_curve.classification_learner |
| Construct an ROC curve using a trained multi-task classification learner on the given data | roc_curve.classification_multi_learner |
| Construct an ROC curve from predicted probabilities and true labels | roc_curve.default |
| Construct an ROC curve using a trained 'glmnetcv_classifier' on the given data | roc_curve.glmnetcv_classifier |
| Generic function for calculating scores | score |
| Calculate the scores for a categorical reward estimator on the given data | score.categorical_reward_estimator |
| Calculate the score for a set of predictions on the given data | score.default |
| Calculate the score for a GLMNet learner on the given data | score.glmnetcv_learner |
| Calculate the scores for a numeric reward estimator on the given data | score.numeric_reward_estimator |
| Calculate the score for an Optimal Feature Selection learner on the given data | score.optimal_feature_selection_learner |
| Calculate the score for a model on the given data | score.supervised_learner |
| Calculate the score for a multi-task model on the given data | score.supervised_multi_learner |
| Show the probability of a specified label when visualizing a learner | set_display_label |
| Set the random seed in Julia | set_julia_seed |
| Set all supplied parameters on a learner | set_params |
| Save a new reward kernel bandwidth inside a learner, and return new reward predictions generated using this bandwidth for the original data used to train the learner. | set_reward_kernel_bandwidth |
| Sets a global rich output parameter | set_rich_output_param |
| For a binary classification problem, update the the predicted labels in the leaves of the learner to predict a label only if the predicted probability is at least the specified threshold. | set_threshold |
| Generic function for showing interactive visualization in browser | show_in_browser |
| Show interactive visualization of an object in the default browser | show_in_browser.abstract_visualization |
| Show interactive visualization of a 'roc_curve' in the default browser | show_in_browser.roc_curve |
| Show interactive tree visualization of a tree learner in the default browser | show_in_browser.tree_learner |
| Generic function for showing interactive questionnaire in browser | show_questionnaire |
| Show an interactive questionnaire based on an Optimal Feature Selection learner in default browser | show_questionnaire.optimal_feature_selection_learner |
| Show an interactive questionnaire based on a tree learner in default browser | show_questionnaire.tree_learner |
| Conduct a similarity comparison between the final tree in a learner and all trees in a new learner to consider the tradeoff between training performance and similarity to the original tree | similarity_comparison |
| Learner for conducting heuristic k-NN imputation | single_knn_imputation_learner |
| Split the data into training and test datasets | split_data |
| Conduct a stability analysis of the trees in a tree learner | stability_analysis |
| Impute missing values in a dataframe using a fitted imputation model | transform |
| Transform features with a trained imputation learner and create adaptive indicator features to encode the missing pattern | transform_and_expand |
| Specify an interactive tree visualization of a tree learner | tree_plot |
| Conduct the reward kernel bandwidth tuning procedure for a range of starting bandwidths and return the final tuned values. | tune_reward_kernel_bandwidth |
| Generic function for calculating variable importance | variable_importance |
| Calculate similarity between the final tree in a tree learner with all trees in new tree learner using variable importance scores. | variable_importance_similarity |
| Generate a ranking of the variables in a learner according to their importance during training. The results are normalized so that they sum to one. | variable_importance.learner |
| Generate a ranking of the variables in an Optimal Feature Selection learner according to their importance during training. The results are normalized so that they sum to one. | variable_importance.optimal_feature_selection_learner |
| Generate a ranking of the variables in a tree learner according to their importance during training. The results are normalized so that they sum to one. | variable_importance.tree_learner |
| Write the internal booster saved in the learner to file | write_booster |
| Output a learner in .dot format | write_dot |
| Generic function for writing interactive visualization to file | write_html |
| Output an object as an interactive browser visualization in HTML format | write_html.abstract_visualization |
| Output an ROC curve as an interactive browser visualization in HTML format | write_html.roc_curve |
| Output a tree learner as an interactive browser visualization in HTML format | write_html.tree_learner |
| Output a learner or grid in JSON format | write_json |
| Output a learner as a PDF image | write_pdf |
| Output a learner as a PNG image | write_png |
| Generic function for writing interactive questionnaire to file | write_questionnaire |
| Output an Optimal Feature Selection learner as an interactive questionnaire in HTML format | write_questionnaire.optimal_feature_selection_learner |
| Output a tree learner as an interactive questionnaire in HTML format | write_questionnaire.tree_learner |
| Output a learner as a SVG image | write_svg |
| Learner for training XGBoost models for classification problems | xgboost_classifier |
| Learner for training XGBoost models for regression problems | xgboost_regressor |
| Learner for training XGBoost models for survival problems | xgboost_survival_learner |
| Learner for conducting zero-imputation | zero_imputation_learner |
