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