Acquire an IAI license for the current session.

Description

Julia Equivalent: IAI.acquire_license

Usage

acquire_license(...)

Arguments

IAI Compatibility

Requires IAI version 3.1 or higher.

Examples

## Not run: iai::acquire_license()

Add additional Julia worker processes to parallelize workloads

Description

Julia Equivalent: Distributed.addprocs!

Usage

add_julia_processes(...)

Arguments

Details

For more information, refer to the documentation on parallelization

Examples

## Not run: iai::add_julia_processes(3)

Return a dataframe containing all treatment combinations of one or more treatment vectors, ready for use as treatment candidates in 'fit_predict!' or 'predict'

Description

Julia Equivalent: IAI.all_treatment_combinations

Usage

all_treatment_combinations(...)

Arguments

Examples

## Not run: iai::all_treatment_combinations(c(1, 2, 3))

Return the leaf index in a tree model into which each point in the features falls

Description

Julia Equivalent: IAI.apply

Usage

apply(lnr, X)

Arguments

Examples

## Not run: iai::apply(lnr, X)

Return the indices of the points in the features that fall into each node of a trained tree model

Description

Julia Equivalent: IAI.apply_nodes

Usage

apply_nodes(lnr, X)

Arguments

Examples

## Not run: iai::apply_nodes(lnr, X)

Convert a vector of values to IAI mixed data format

Description

Julia Equivalent: IAI.make_mixed_data

Usage

as.mixeddata(values, categorical_levels, ordinal_levels = c())

Arguments

Examples

## Not run: 
df <- iris
set.seed(1)
df$mixed <- rnorm(150)
df$mixed[1:5] <- NA  # Insert some missing values
df$mixed[6:10] <- "Not graded"
df$mixed <- iai::as.mixeddata(df$mixed, c("Not graded"))

## End(Not run)

Construct a ggplot2::ggplot object plotting grid search results for Optimal Feature Selection learners

Description

Construct a ggplot2::ggplot object plotting grid search results for Optimal Feature Selection learners

Usage

## S3 method for class 'grid_search'
autoplot(object, type = stop("`type` is required"), ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: ggplot2::autoplot(grid)

Construct a ggplot2::ggplot object plotting the ROC curve

Description

Construct a ggplot2::ggplot object plotting the ROC curve

Usage

## S3 method for class 'roc_curve'
autoplot(object, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: ggplot2::autoplot(roc)

Construct a ggplot2::ggplot object plotting the results of the similarity comparison

Description

Construct a ggplot2::ggplot object plotting the results of the similarity comparison

Usage

## S3 method for class 'similarity_comparison'
autoplot(object, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: ggplot2::autoplot(similarity)

Construct a ggplot2::ggplot object plotting the results of the stability analysis

Description

Construct a ggplot2::ggplot object plotting the results of the stability analysis

Usage

## S3 method for class 'stability_analysis'
autoplot(object, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: ggplot2::autoplot(stability)

Learner for conducting reward estimation with categorical treatments and classification outcomes

Description

Julia Equivalent: IAI.CategoricalClassificationRewardEstimator

Usage

categorical_classification_reward_estimator(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::categorical_classification_reward_estimator()

Learner for conducting reward estimation with categorical treatments and regression outcomes

Description

Julia Equivalent: IAI.CategoricalRegressionRewardEstimator

Usage

categorical_regression_reward_estimator(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::categorical_regression_reward_estimator()

Learner for conducting reward estimation with categorical treatments

Description

This function was deprecated in iai 1.6.0, and [categorical_classification_reward_estimator()] or [categorical_classification_reward_estimator()] should be used instead.

Usage

categorical_reward_estimator(...)

Arguments

Details

This deprecation is no longer supported as of the IAI v3 release.

IAI Compatibility

Requires IAI version 2.0, 2.1 or 2.2.

Examples

## Not run: lnr <- iai::categorical_reward_estimator()

Learner for conducting reward estimation with categorical treatments and survival outcomes

Description

Julia Equivalent: IAI.CategoricalSurvivalRewardEstimator

Usage

categorical_survival_reward_estimator(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::categorical_survival_reward_estimator()

Remove all traces of automatic Julia/IAI installation

Description

Removes files created by install_julia and install_system_image

Usage

cleanup_installation()

Examples

## Not run: iai::cleanup_installation()

Return an unfitted copy of a learner with the same parameters

Description

Julia Equivalent: IAI.clone

Usage

clone(lnr)

Arguments

Examples

## Not run: new_lnr <- iai::clone(lnr)

Convert 'treatments' from symbol/string format into numeric values.

Description

Julia Equivalent: IAI.convert_treatments_to_numeric

Usage

convert_treatments_to_numeric(treatments)

Arguments

Examples

## Not run: iai::convert_treatments_to_numeric(c("1", "2", "3"))

Copy the tree split structure from one learner into another and refit the models in each leaf of the tree using the supplied data

Description

Julia Equivalent: IAI.copy_splits_and_refit_leaves!

Usage

copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::copy_splits_and_refit_leaves(new_lnr, orig_lnr, ...)

Return a matrix where entry (i, j) is true if the ith point in the features passes through the jth node in a trained tree model.

Description

Julia Equivalent: IAI.decision_path

Usage

decision_path(lnr, X)

Arguments

Examples

## Not run: iai::decision_path(lnr, X)

Delete a global rich output parameter

Description

Julia Equivalent: IAI.delete_rich_output_param!

Usage

delete_rich_output_param(key)

Arguments

Examples

## Not run: iai::delete_rich_output_param("simple_layout")

Learner that estimates equal propensity for all treatments.

Description

For use with data from randomized experiments where treatments are known to be randomly assigned.

Usage

equal_propensity_estimator(...)

Arguments

Details

Julia Equivalent: IAI.EqualPropensityEstimator

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::equal_propensity_estimator()

Generic function for fitting a learner.

Description

Generic function for fitting a learner.

Usage

fit(obj, ...)

Arguments

Fits a grid_search to the training data

Description

Julia Equivalent: IAI.fit!

Usage

## S3 method for class 'grid_search'
fit(obj, X, ...)

Arguments

Examples

## Not run: 
X <- iris[, 1:4]
y <- iris$Species
grid <- iai::grid_search(
    iai::optimal_tree_classifier(max_depth = 1),
)
iai::fit(grid, X, y)

## End(Not run)

Fits an imputation learner to the training data.

Description

Additional keyword arguments are available for fitting imputation learners - please refer to the Julia documentation.

Usage

## S3 method for class 'imputation_learner'
fit(obj, X, ...)

Arguments

Details

Julia Equivalent: IAI.fit!

Examples

## Not run: iai::fit(lnr, X)

Fits a model to the training data

Description

Julia Equivalent: IAI.fit!

Usage

## S3 method for class 'learner'
fit(obj, X, ...)

Arguments

Examples

## Not run: iai::fit(lnr, X, y)

Fits an Optimal Feature Selection learner to the training data

Description

When the coordinated_sparsity parameter of the learner is TRUE, additional keyword arguments are required - please refer to the Julia documentation.

Usage

## S3 method for class 'optimal_feature_selection_learner'
fit(obj, X, ...)

Arguments

Details

Julia Equivalent: IAI.fit!

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::fit(lnr, X)

Fit an imputation learner with training features and create adaptive indicator features to encode the missing pattern

Description

Julia Equivalent: IAI.fit_and_expand!

Usage

fit_and_expand(lnr, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: lnr <- iai::fit_and_expand(lnr, X, type = "finite")

Fits a grid search to the training data with cross-validation

Description

Julia Equivalent: IAI.fit_cv!

Usage

fit_cv(grid, X, ...)

Arguments

Examples

## Not run: 
X <- iris[, 1:4]
y <- iris$Species
grid <- iai::grid_search(
    iai::optimal_tree_classifier(max_depth = 1),
)
iai::fit_cv(grid, X, y)

## End(Not run)

Generic function for fitting a reward estimator on features, treatments and returning predicted counterfactual rewards and scores of the internal estimators.

Description

Julia Equivalent: IAI.fit_predict!

Usage

fit_predict(obj, ...)

Arguments

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.

Description

Julia Equivalent: IAI.fit_predict!

Usage

## S3 method for class 'categorical_reward_estimator'
fit_predict(obj, X, treatments, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::fit_predict(obj, X, treatments, outcomes)

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.

Description

Julia Equivalent: IAI.fit_predict!

Usage

## S3 method for class 'numeric_reward_estimator'
fit_predict(obj, X, treatments, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::fit_predict(obj, X, treatments, outcomes)

Fit an imputation model using the given features and impute the missing values in these features

Description

Similar to calling fit.imputation_learner followed by transform

Usage

fit_transform(lnr, X, ...)

Arguments

Details

Julia Equivalent: IAI.fit_transform!

Examples

## Not run: 
X <- iris
X[1, 1] <- NA
grid <- iai::grid_search(
    iai::imputation_learner(),
    method = c("opt_knn", "opt_tree"),
)
iai::fit_transform(grid, X)

## End(Not run)

Train a grid using cross-validation with features and impute all missing values in these features

Description

Julia Equivalent: IAI.fit_transform_cv!

Usage

fit_transform_cv(grid, X, ...)

Arguments

Examples

## Not run: 
X <- iris
X[1, 1] <- NA
grid <- iai::grid_search(
    iai::imputation_learner(),
    method = c("opt_knn", "opt_tree"),
)
iai::fit_transform_cv(grid, X)

## End(Not run)

Return the best parameter combination from a grid

Description

Julia Equivalent: IAI.get_best_params

Usage

get_best_params(grid)

Arguments

Examples

## Not run: iai::get_best_params(grid)

Generic function for returning the predicted label in the node of a classification tree

Description

Generic function for returning the predicted label in the node of a classification tree

Usage

get_classification_label(obj, ...)

Arguments

Return the predicted label at a node of a tree

Description

Julia Equivalent: IAI.get_classification_label

Usage

## S3 method for class 'classification_tree_learner'
get_classification_label(obj, node_index, ...)

Arguments

Examples

## Not run: iai::get_classification_label(lnr, 1)

Return the predicted label at a node of a multi-task tree

Description

Julia Equivalent: IAI.get_classification_label and IAI.get_classification_label

Usage

## S3 method for class 'classification_tree_multi_learner'
get_classification_label(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_classification_label(lnr, 1)

Generic function for returning the probabilities of class membership at a node of a classification tree

Description

Generic function for returning the probabilities of class membership at a node of a classification tree

Usage

get_classification_proba(obj, ...)

Arguments

Return the predicted probabilities of class membership at a node of a tree

Description

Julia Equivalent: IAI.get_classification_proba

Usage

## S3 method for class 'classification_tree_learner'
get_classification_proba(obj, node_index, ...)

Arguments

Examples

## Not run: iai::get_classification_proba(lnr, 1)

Return the predicted probabilities of class membership at a node of a multi-task tree

Description

Julia Equivalent: IAI.get_classification_proba and IAI.get_classification_proba

Usage

## S3 method for class 'classification_tree_multi_learner'
get_classification_proba(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_classification_proba(lnr, 1)

Return the indices of the trees assigned to each cluster, under the clustering of a given number of trees

Description

Julia Equivalent: IAI.get_cluster_assignments

Usage

get_cluster_assignments(stability, num_trees)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_cluster_assignments(stability, num_trees)

Return the centroid information for each cluster, under the clustering of a given number of trees

Description

Julia Equivalent: IAI.get_cluster_details

Usage

get_cluster_details(stability, num_trees)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_cluster_details(stability, num_trees)

Return the distances between the centroids of each pair of clusters, under the clustering of a given number of trees

Description

Julia Equivalent: IAI.get_cluster_distances

Usage

get_cluster_distances(stability, num_trees)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_cluster_distances(stability, num_trees)

Get the depth of a node of a tree

Description

Julia Equivalent: IAI.get_depth

Usage

get_depth(lnr, node_index)

Arguments

Examples

## Not run: iai::get_depth(lnr, 1)

Return the total kernel density surrounding each treatment candidate for the propensity/outcome estimation problems in a fitted learner.

Description

Julia Equivalent: IAI.get_estimation_densities

Usage

get_estimation_densities(lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_estimation_densities(lnr, ...)

Return the names of the features used by the learner

Description

Julia Equivalent: IAI.get_features_used

Usage

get_features_used(lnr)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_features_used(lnr)

Return a vector of lists detailing the results of the grid search

Description

Julia Equivalent: IAI.get_grid_result_details

Usage

get_grid_result_details(grid)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_grid_result_details(grid)

Return a summary of the results from the grid search

Description

Julia Equivalent: IAI.get_grid_result_summary

Usage

get_grid_result_summary(grid)

Arguments

Examples

## Not run: iai::get_grid_result_summary(grid)

Return a summary of the results from the grid search

Description

This function was deprecated and renamed to [get_grid_result_summary()] in iai 1.5.0. This is for consistency with the IAI v2.2.0 Julia release.

Usage

get_grid_results(grid)

Arguments

Examples

## Not run: iai::get_grid_results(grid)

Return the fitted learner using the best parameter combination from a grid

Description

Julia Equivalent: IAI.get_learner

Usage

get_learner(grid)

Arguments

Examples

## Not run: lnr <- iai::get_learner(grid)

Get the index of the lower child at a split node of a tree

Description

Julia Equivalent: IAI.get_lower_child

Usage

get_lower_child(lnr, node_index)

Arguments

Examples

## Not run: iai::get_lower_child(lnr, 1)

Return the machine ID for the current computer.

Description

This ID ties the IAI license file to your machine.

Usage

get_machine_id()

Examples

## Not run: iai::get_machine_id()

Generic function for returning the number of fits in a trained learner

Description

Generic function for returning the number of fits in a trained learner

Usage

get_num_fits(obj, ...)

Arguments

Return the number of fits along the path in a trained GLMNet learner

Description

Julia Equivalent: IAI.get_num_fits

Usage

## S3 method for class 'glmnetcv_learner'
get_num_fits(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::get_num_fits(lnr)

Return the number of fits along the path in a trained Optimal Feature Selection learner

Description

Julia Equivalent: IAI.get_num_fits

Usage

## S3 method for class 'optimal_feature_selection_learner'
get_num_fits(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::get_num_fits(lnr)

Return the number of nodes in a trained learner

Description

Julia Equivalent: IAI.get_num_nodes

Usage

get_num_nodes(lnr)

Arguments

Examples

## Not run: iai::get_num_nodes(lnr)

Get the number of training points contained in a node of a tree

Description

Julia Equivalent: IAI.get_num_samples

Usage

get_num_samples(lnr, node_index)

Arguments

Examples

## Not run: iai::get_num_samples(lnr, 1)

Return the value of all parameters on a learner

Description

Julia Equivalent: IAI.get_params

Usage

get_params(lnr)

Arguments

Examples

## Not run: iai::get_params(lnr)

Get the index of the parent node at a node of a tree

Description

Julia Equivalent: IAI.get_parent

Usage

get_parent(lnr, node_index)

Arguments

Examples

## Not run: iai::get_parent(lnr, 2)

Return the quality of the treatments at a node of a tree

Description

Julia Equivalent: IAI.get_policy_treatment_outcome

Usage

get_policy_treatment_outcome(lnr, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::get_policy_treatment_outcome(lnr, 1)

Return the standard error for the quality of the treatments at a node of a tree

Description

Julia Equivalent: IAI.get_policy_treatment_outcome_standard_error

Usage

get_policy_treatment_outcome_standard_error(lnr, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_policy_treatment_outcome_standard_error(lnr, 1)

Return the treatments ordered from most effective to least effective at a node of a tree

Description

Julia Equivalent: IAI.get_policy_treatment_rank

Usage

get_policy_treatment_rank(lnr, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::get_policy_treatment_rank(lnr, 1)

Generic function for returning the prediction constant in a trained learner

Description

Generic function for returning the prediction constant in a trained learner

Usage

get_prediction_constant(obj, ...)

Arguments

Return the constant term in the prediction in a trained GLMNet learner

Description

Julia Equivalent: IAI.get_prediction_constant

Usage

## S3 method for class 'glmnetcv_learner'
get_prediction_constant(obj, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::get_prediction_constant(lnr)

Return the constant term in the prediction in a trained Optimal Feature Selection learner

Description

Julia Equivalent: IAI.get_prediction_constant

Usage

## S3 method for class 'optimal_feature_selection_learner'
get_prediction_constant(obj, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::get_prediction_constant(lnr)

Generic function for returning the prediction weights in a trained learner

Description

Generic function for returning the prediction weights in a trained learner

Usage

get_prediction_weights(obj, ...)

Arguments

Return the weights for numeric and categoric features used for prediction in a trained GLMNet learner

Description

Julia Equivalent: IAI.get_prediction_weights

Usage

## S3 method for class 'glmnetcv_learner'
get_prediction_weights(obj, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::get_prediction_weights(lnr)

Return the weights for numeric and categoric features used for prediction in a trained Optimal Feature Selection learner

Description

Julia Equivalent: IAI.get_prediction_weights

Usage

## S3 method for class 'optimal_feature_selection_learner'
get_prediction_weights(obj, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::get_prediction_weights(lnr)

Return the treatments ordered from most effective to least effective at a node of a tree

Description

Julia Equivalent: IAI.get_prescription_treatment_rank

Usage

get_prescription_treatment_rank(lnr, node_index, ...)

Arguments

Examples

## Not run: iai::get_prescription_treatment_rank(lnr, 1)

Generic function for returning the constant term in the regression prediction at a node of a tree

Description

Generic function for returning the constant term in the regression prediction at a node of a tree

Usage

get_regression_constant(obj, ...)

Arguments

Return the constant term in the logistic regression prediction at a node of a classification tree

Description

Julia Equivalent: IAI.get_regression_constant

Usage

## S3 method for class 'classification_tree_learner'
get_regression_constant(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::get_regression_constant(lnr, 1)

Return the constant term in the logistic regression prediction at a node of a multi-task classification tree

Description

Julia Equivalent: IAI.get_regression_constant and IAI.get_regression_constant

Usage

## S3 method for class 'classification_tree_multi_learner'
get_regression_constant(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_regression_constant(lnr, 1)

Return the constant term in the linear regression prediction at a node of a prescription tree

Description

Julia Equivalent: IAI.get_regression_constant

Usage

## S3 method for class 'prescription_tree_learner'
get_regression_constant(obj, node_index, treatment, ...)

Arguments

Examples

## Not run: iai::get_regression_constant(lnr, 1, "A")

Return the constant term in the linear regression prediction at a node of a regression tree

Description

Julia Equivalent: IAI.get_regression_constant

Usage

## S3 method for class 'regression_tree_learner'
get_regression_constant(obj, node_index, ...)

Arguments

Examples

## Not run: iai::get_regression_constant(lnr, 1)

Return the constant term in the linear regression prediction at a node of a multi-task regression tree

Description

Julia Equivalent: IAI.get_regression_constant and IAI.get_regression_constant

Usage

## S3 method for class 'regression_tree_multi_learner'
get_regression_constant(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_regression_constant(lnr, 1)

Return the constant term in the cox regression prediction at a node of a survival tree

Description

Julia Equivalent: IAI.get_regression_constant

Usage

## S3 method for class 'survival_tree_learner'
get_regression_constant(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::get_regression_constant(lnr, 1)

Generic function for returning the weights for each feature in the regression prediction at a node of a tree

Description

Generic function for returning the weights for each feature in the regression prediction at a node of a tree

Usage

get_regression_weights(obj, ...)

Arguments

Return the weights for each feature in the logistic regression prediction at a node of a classification tree

Description

Julia Equivalent: IAI.get_regression_weights

Usage

## S3 method for class 'classification_tree_learner'
get_regression_weights(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::get_regression_weights(lnr, 1)

Return the weights for each feature in the logistic regression prediction at a node of a multi-task classification tree

Description

Julia Equivalent: IAI.get_regression_weights and IAI.get_regression_weights

Usage

## S3 method for class 'classification_tree_multi_learner'
get_regression_weights(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_regression_weights(lnr, 1)

Return the weights for each feature in the linear regression prediction at a node of a prescription tree

Description

Julia Equivalent: IAI.get_regression_weights

Usage

## S3 method for class 'prescription_tree_learner'
get_regression_weights(obj, node_index, treatment, ...)

Arguments

Examples

## Not run: iai::get_regression_weights(lnr, 1, "A")

Return the weights for each feature in the linear regression prediction at a node of a regression tree

Description

Julia Equivalent: IAI.get_regression_weights

Usage

## S3 method for class 'regression_tree_learner'
get_regression_weights(obj, node_index, ...)

Arguments

Examples

## Not run: iai::get_regression_weights(lnr, 1)

Return the weights for each feature in the linear regression prediction at a node of a multi-task regression tree

Description

Julia Equivalent: IAI.get_regression_weights and IAI.get_regression_weights

Usage

## S3 method for class 'regression_tree_multi_learner'
get_regression_weights(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::get_regression_weights(lnr, 1)

Return the weights for each feature in the cox regression prediction at a node of a survival tree

Description

Julia Equivalent: IAI.get_regression_weights

Usage

## S3 method for class 'survival_tree_learner'
get_regression_weights(obj, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::get_regression_weights(lnr, 1)

Return the current global rich output parameter settings

Description

Julia Equivalent: IAI.get_rich_output_params

Usage

get_rich_output_params()

Examples

## Not run: iai::get_rich_output_params()

Extract the underlying data from an ROC curve

Description

ROC curves are returned by roc_curve, e.g. roc_curve.classification_learner

Usage

get_roc_curve_data(curve)

Arguments

Details

The data is returned as a list with two keys: auc giving the area-under-the-curve, and coords containing a vector of lists representing each point on the curve, each with keys fpr (the false positive rate), tpr (the true positive rate) and threshold (the threshold).

Julia Equivalent: IAI.get_roc_curve_data

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::get_roc_curve_data(curve)

Return the categoric/ordinal information used in the split at a node of a tree

Description

Julia Equivalent: IAI.get_split_categories

Usage

get_split_categories(lnr, node_index)

Arguments

Examples

## Not run: iai::get_split_categories(lnr, 1)

Return the feature used in the split at a node of a tree

Description

Julia Equivalent: IAI.get_split_feature

Usage

get_split_feature(lnr, node_index)

Arguments

Examples

## Not run: iai::get_split_feature(lnr, 1)

Return the threshold used in the split at a node of a tree

Description

Julia Equivalent: IAI.get_split_threshold

Usage

get_split_threshold(lnr, node_index)

Arguments

Examples

## Not run: iai::get_split_threshold(lnr, 1)

Return the weights for numeric and categoric features used in the hyperplane split at a node of a tree

Description

Julia Equivalent: IAI.get_split_weights

Usage

get_split_weights(lnr, node_index)

Arguments

Examples

## Not run: iai::get_split_weights(lnr, 1)

Return the trained trees in order of increasing objective value, along with their variable importance scores for each feature

Description

Julia Equivalent: IAI.get_stability_results

Usage

get_stability_results(stability)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_stability_results(stability)

Return the survival curve at a node of a tree

Description

Julia Equivalent: IAI.get_survival_curve

Usage

get_survival_curve(lnr, node_index, ...)

Arguments

Examples

## Not run: iai::get_survival_curve(lnr, 1)

Extract the underlying data from a survival curve (as returned by predict.survival_learner or get_survival_curve)

Description

The data is returned as a list with two keys: times containing the time for each breakpoint on the curve, and coefs containing the probability for each breakpoint on the curve.

Usage

get_survival_curve_data(curve)

Arguments

Details

Julia Equivalent: IAI.get_survival_curve_data

Examples

## Not run: iai::get_survival_curve_data(curve)

Return the predicted expected survival time at a node of a tree

Description

Julia Equivalent: IAI.get_survival_expected_time

Usage

get_survival_expected_time(lnr, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::get_survival_expected_time(lnr, 1)

Return the predicted hazard ratio at a node of a tree

Description

Julia Equivalent: IAI.get_survival_hazard

Usage

get_survival_hazard(lnr, node_index, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::get_survival_hazard(lnr, 1)

Extract the training objective value for each candidate tree in the comparison, where a lower value indicates a better solution

Description

Julia Equivalent: IAI.get_train_errors

Usage

get_train_errors(similarity)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_train_errors(similarity)

Return a copy of the learner that uses a specific tree rather than the tree with the best training objective.

Description

Julia Equivalent: IAI.get_tree

Usage

get_tree(lnr, index)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::get_tree(lnr, index)

Get the index of the upper child at a split node of a tree

Description

Julia Equivalent: IAI.get_upper_child

Usage

get_upper_child(lnr, node_index)

Arguments

Examples

## Not run: iai::get_upper_child(lnr, 1)

Learner for training GLMNet models for classification problems with cross-validation

Description

Julia Equivalent: IAI.GLMNetCVClassifier

Usage

glmnetcv_classifier(...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: lnr <- iai::glmnetcv_classifier()

Learner for training GLMNet models for regression problems with cross-validation

Description

Julia Equivalent: IAI.GLMNetCVRegressor

Usage

glmnetcv_regressor(...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::glmnetcv_regressor()

Learner for training GLMNet models for survival problems with cross-validation

Description

Julia Equivalent: IAI.GLMNetCVSurvivalLearner

Usage

glmnetcv_survival_learner(...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: lnr <- iai::glmnetcv_survival_learner()

Controls grid search over parameter combinations

Description

Julia Equivalent: IAI.GridSearch

Usage

grid_search(lnr, ...)

Arguments

Examples

## Not run: 
grid <- iai::grid_search(
    iai::optimal_tree_classifier(
        random_seed = 1,
    ),
    max_depth = 1:5,
)

## End(Not run)

Initialize Julia and the IAI package.

Description

This function is called automatically with default parameters the first time any 'iai' function is used in an R session. If custom parameters for Julia setup are required, this function must be called in every R session before calling other 'iai' functions.

Usage

iai_setup(...)

Arguments

Examples

## Not run: iai::iai_setup()

Generic learner for imputing missing values

Description

Julia Equivalent: IAI.ImputationLearner

Usage

imputation_learner(method = "opt_knn", ...)

Arguments

Examples

## Not run: lnr <- iai::imputation_learner(method = "opt_tree")

Impute missing values using either a specified method or through validation

Description

Julia Equivalent: IAI.impute

Usage

impute(X, ...)

Arguments

Details

This function was deprecated in iai 1.7.0. This is for consistency with the IAI v3.0.0 Julia release.

Examples

## Not run: 
X <- iris
X[1, 1] <- NA
iai::impute(X)

## End(Not run)

Impute missing values using cross validation

Description

Julia Equivalent: IAI.impute_cv

Usage

impute_cv(X, ...)

Arguments

Details

This function was deprecated in iai 1.7.0. This is for consistency with the IAI v3.0.0 Julia release.

Examples

## Not run: 
X <- iris
X[1, 1] <- NA
iai::impute_cv(X, list(method = c("opt_knn", "opt_tree")))

## End(Not run)

Download and install Julia automatically.

Description

Download and install Julia automatically.

Usage

install_julia(version = "latest", prefix = julia_default_install_dir())

Arguments

Examples

## Not run: iai::install_julia()

Download and install the IAI system image automatically.

Description

Download and install the IAI system image automatically.

Usage

install_system_image(
  version = "latest",
  replace_default = FALSE,
  prefix = sysimage_default_install_dir(),
  accept_license = FALSE
)

Arguments

Examples

## Not run: iai::install_system_image()

Check if a node of a tree applies a categoric split

Description

Julia Equivalent: IAI.is_categoric_split

Usage

is_categoric_split(lnr, node_index)

Arguments

Examples

## Not run: iai::is_categoric_split(lnr, 1)

Check if a node of a tree applies a hyperplane split

Description

Julia Equivalent: IAI.is_hyperplane_split

Usage

is_hyperplane_split(lnr, node_index)

Arguments

Examples

## Not run: iai::is_hyperplane_split(lnr, 1)

Check if a node of a tree is a leaf

Description

Julia Equivalent: IAI.is_leaf

Usage

is_leaf(lnr, node_index)

Arguments

Examples

## Not run: iai::is_leaf(lnr, 1)

Check if a node of a tree applies a mixed ordinal/categoric split

Description

Julia Equivalent: IAI.is_mixed_ordinal_split

Usage

is_mixed_ordinal_split(lnr, node_index)

Arguments

Examples

## Not run: iai::is_mixed_ordinal_split(lnr, 1)

Check if a node of a tree applies a mixed parallel/categoric split

Description

Julia Equivalent: IAI.is_mixed_parallel_split

Usage

is_mixed_parallel_split(lnr, node_index)

Arguments

Examples

## Not run: iai::is_mixed_parallel_split(lnr, 1)

Check if a node of a tree applies a ordinal split

Description

Julia Equivalent: IAI.is_ordinal_split

Usage

is_ordinal_split(lnr, node_index)

Arguments

Examples

## Not run: iai::is_ordinal_split(lnr, 1)

Check if a node of a tree applies a parallel split

Description

Julia Equivalent: IAI.is_parallel_split

Usage

is_parallel_split(lnr, node_index)

Arguments

Examples

## Not run: iai::is_parallel_split(lnr, 1)

Loads the Julia Graphviz library to permit certain visualizations.

Description

The library will be installed if not already present.

Usage

load_graphviz()

Examples

## Not run: iai::load_graphviz()

Learner for conducting mean imputation

Description

Julia Equivalent: IAI.MeanImputationLearner

Usage

mean_imputation_learner(...)

Arguments

Examples

## Not run: lnr <- iai::mean_imputation_learner()

Check if points with missing values go to the lower child at a split node of of a tree

Description

Julia Equivalent: IAI.missing_goes_lower

Usage

missing_goes_lower(lnr, node_index)

Arguments

Examples

## Not run: iai::missing_goes_lower(lnr, 1)

Generic function for constructing an interactive questionnaire with multiple learners

Description

Generic function for constructing an interactive questionnaire with multiple learners

Usage

multi_questionnaire(obj, ...)

Arguments

Construct an interactive questionnaire from multiple specified learners

Description

Refer to the documentation on advanced tree visualization for more information.

Usage

## Default S3 method:
multi_questionnaire(obj, ...)

Arguments

Details

Julia Equivalent: IAI.MultiQuestionnaire

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: 
iai::multi_questionnaire(list("Questionnaire for" = list(
   "first learner" = lnr1,
   "second learner" = lnr2
)))

## End(Not run)

Construct an interactive tree questionnaire using multiple learners from the results of a grid search

Description

Julia Equivalent: IAI.MultiQuestionnaire

Usage

## S3 method for class 'grid_search'
multi_questionnaire(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::multi_questionnaire(grid)

Generic function for constructing an interactive tree visualization of multiple tree learners

Description

Generic function for constructing an interactive tree visualization of multiple tree learners

Usage

multi_tree_plot(obj, ...)

Arguments

Construct an interactive tree visualization of multiple tree learners as specified by questions

Description

Refer to the documentation on advanced tree visualization for more information.

Usage

## Default S3 method:
multi_tree_plot(obj, ...)

Arguments

Details

Julia Equivalent: IAI.MultiTreePlot

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: 
iai::multi_tree_plot(list("Visualizing" = list(
   "first learner" = lnr1,
   "second learner" = lnr2
)))

## End(Not run)

Construct an interactive tree visualization of multiple tree learners from the results of a grid search

Description

Julia Equivalent: IAI.MultiTreePlot

Usage

## S3 method for class 'grid_search'
multi_tree_plot(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::multi_tree_plot(grid)

Learner for conducting reward estimation with numeric treatments and classification outcomes

Description

Julia Equivalent: IAI.NumericClassificationRewardEstimator

Usage

numeric_classification_reward_estimator(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::numeric_classification_reward_estimator()

Learner for conducting reward estimation with numeric treatments and regression outcomes

Description

Julia Equivalent: IAI.NumericRegressionRewardEstimator

Usage

numeric_regression_reward_estimator(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::numeric_regression_reward_estimator()

Learner for conducting reward estimation with numeric treatments

Description

This function was deprecated in iai 1.6.0, and [numeric_classification_reward_estimator()] or [numeric_classification_reward_estimator()] should be used instead.

Usage

numeric_reward_estimator(...)

Arguments

Details

This deprecation is no longer supported as of the IAI v3 release.

IAI Compatibility

Requires IAI version 2.1 or 2.2.

Examples

## Not run: lnr <- iai::numeric_reward_estimator()

Learner for conducting reward estimation with numeric treatments and survival outcomes

Description

Julia Equivalent: IAI.NumericSurvivalRewardEstimator

Usage

numeric_survival_reward_estimator(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::numeric_survival_reward_estimator()

Learner for conducting optimal k-NN imputation

Description

Julia Equivalent: IAI.OptKNNImputationLearner

Usage

opt_knn_imputation_learner(...)

Arguments

Examples

## Not run: lnr <- iai::opt_knn_imputation_learner()

Learner for conducting optimal SVM imputation

Description

Julia Equivalent: IAI.OptSVMImputationLearner

Usage

opt_svm_imputation_learner(...)

Arguments

Examples

## Not run: lnr <- iai::opt_svm_imputation_learner()

Learner for conducting optimal tree-based imputation

Description

Julia Equivalent: IAI.OptTreeImputationLearner

Usage

opt_tree_imputation_learner(...)

Arguments

Examples

## Not run: lnr <- iai::opt_tree_imputation_learner()

Learner for conducting Optimal Feature Selection on classification problems

Description

Julia Equivalent: IAI.OptimalFeatureSelectionClassifier

Usage

optimal_feature_selection_classifier(...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: lnr <- iai::optimal_feature_selection_classifier()

Learner for conducting Optimal Feature Selection on regression problems

Description

Julia Equivalent: IAI.OptimalFeatureSelectionRegressor

Usage

optimal_feature_selection_regressor(...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: lnr <- iai::optimal_feature_selection_regressor()

Learner for training Optimal Classification Trees

Description

Julia Equivalent: IAI.OptimalTreeClassifier

Usage

optimal_tree_classifier(...)

Arguments

Examples

## Not run: lnr <- iai::optimal_tree_classifier()

Learner for training multi-task Optimal Classification Trees

Description

Julia Equivalent: IAI.OptimalTreeMultiClassifier

Usage

optimal_tree_multi_classifier(...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: lnr <- iai::optimal_tree_multi_classifier()

Learner for training multi-task Optimal Regression Trees

Description

Julia Equivalent: IAI.OptimalTreeMultiRegressor

Usage

optimal_tree_multi_regressor(...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: lnr <- iai::optimal_tree_multi_regressor()

Learner for training Optimal Policy Trees where the policy should aim to maximize outcomes

Description

Julia Equivalent: IAI.OptimalTreePolicyMaximizer

Usage

optimal_tree_policy_maximizer(...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: lnr <- iai::optimal_tree_policy_maximizer()

Learner for training Optimal Policy Trees where the policy should aim to minimize outcomes

Description

Julia Equivalent: IAI.OptimalTreePolicyMinimizer

Usage

optimal_tree_policy_minimizer(...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: lnr <- iai::optimal_tree_policy_minimizer()

Learner for training Optimal Prescriptive Trees where the prescriptions should aim to maximize outcomes

Description

Julia Equivalent: IAI.OptimalTreePrescriptionMaximizer

Usage

optimal_tree_prescription_maximizer(...)

Arguments

Examples

## Not run: lnr <- iai::optimal_tree_prescription_maximizer()

Learner for training Optimal Prescriptive Trees where the prescriptions should aim to minimize outcomes

Description

Julia Equivalent: IAI.OptimalTreePrescriptionMinimizer

Usage

optimal_tree_prescription_minimizer(...)

Arguments

Examples

## Not run: lnr <- iai::optimal_tree_prescription_minimizer()

Learner for training Optimal Regression Trees

Description

Julia Equivalent: IAI.OptimalTreeRegressor

Usage

optimal_tree_regressor(...)

Arguments

Examples

## Not run: lnr <- iai::optimal_tree_regressor()

Learner for training Optimal Survival Trees

Description

Julia Equivalent: IAI.OptimalTreeSurvivalLearner

Usage

optimal_tree_survival_learner(...)

Arguments

Examples

## Not run: lnr <- iai::optimal_tree_survival_learner()

Learner for training Optimal Survival Trees

Description

This function was deprecated and renamed to optimal_tree_survival_learner() in iai 1.3.0. This is for consistency with the IAI v2.0.0 Julia release.

Usage

optimal_tree_survivor(...)

Arguments

Examples

## Not run: lnr <- iai::optimal_tree_survivor()

Plot a grid search results for Optimal Feature Selection learners

Description

Plot a grid search results for Optimal Feature Selection learners

Usage

## S3 method for class 'grid_search'
plot(x, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: plot(grid)

Plot an ROC curve

Description

Plot an ROC curve

Usage

## S3 method for class 'roc_curve'
plot(x, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: plot(roc)

Plot a similarity comparison

Description

Plot a similarity comparison

Usage

## S3 method for class 'similarity_comparison'
plot(x, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: plot(similarity)

Plot a stability analysis

Description

Plot a stability analysis

Usage

## S3 method for class 'stability_analysis'
plot(x, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: plot(stability)

Generic function for returning the predictions of a model

Description

Generic function for returning the predictions of a model

Usage

predict(obj, ...)

Arguments

Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data

Description

Julia Equivalent: IAI.predict

Usage

## S3 method for class 'categorical_reward_estimator'
predict(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::predict(lnr, X, treatments, outcomes)

Return the predictions made by a GLMNet learner for each point in the features

Description

Julia Equivalent: IAI.predict

Usage

## S3 method for class 'glmnetcv_learner'
predict(obj, X, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::predict(lnr, X)

Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data

Description

Julia Equivalent: IAI.predict

Usage

## S3 method for class 'numeric_reward_estimator'
predict(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::predict(lnr, X, treatments, outcomes)

Return the predictions made by an Optimal Feature Selection learner for each point in the features

Description

Julia Equivalent: IAI.predict

Usage

## S3 method for class 'optimal_feature_selection_learner'
predict(obj, X, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::predict(lnr, X)

Return the predictions made by a supervised learner for each point in the features

Description

Julia Equivalent: IAI.predict

Usage

## S3 method for class 'supervised_learner'
predict(obj, X, ...)

Arguments

Examples

## Not run: iai::predict(lnr, X)

Return the predictions made by a multi-task supervised learner for each point in the features

Description

Julia Equivalent: IAI.predict and IAI.predict

Usage

## S3 method for class 'supervised_multi_learner'
predict(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::predict(lnr, X)

Return the predictions made by a survival learner for each point in the features

Description

Julia Equivalent: IAI.predict

Usage

## S3 method for class 'survival_learner'
predict(obj, X, t = NULL, ...)

Arguments

Examples

## Not run: iai::predict(lnr, X, t = 10)

Generic function for returning the expected survival time predicted by a model

Description

Generic function for returning the expected survival time predicted by a model

Usage

predict_expected_survival_time(obj, ...)

Arguments

Return the expected survival time estimate made by a glmnetcv_survival_learner for each point in the features.

Description

Julia Equivalent: IAI.predict_expected_survival_time

Usage

## S3 method for class 'glmnetcv_survival_learner'
predict_expected_survival_time(obj, X, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::predict_expected_survival_time(lnr, X)

Return the expected survival time estimate made by a survival curve (as returned by predict.survival_learner or get_survival_curve)

Description

Julia Equivalent: IAI.predict_expected_survival_time

Usage

## S3 method for class 'survival_curve'
predict_expected_survival_time(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::predict_expected_survival_time(curve)

Return the expected survival time estimate made by a survival learner for each point in the features.

Description

Julia Equivalent: IAI.predict_expected_survival_time

Usage

## S3 method for class 'survival_learner'
predict_expected_survival_time(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::predict_expected_survival_time(lnr, X)

Generic function for returning the hazard coefficient predicted by a model

Description

Generic function for returning the hazard coefficient predicted by a model

Usage

predict_hazard(obj, ...)

Arguments

Return the fitted hazard coefficient estimate made by a glmnetcv_survival_learner for each point in the features.

Description

A higher hazard coefficient estimate corresponds to a smaller predicted survival time.

Usage

## S3 method for class 'glmnetcv_survival_learner'
predict_hazard(obj, X, fit_index = NULL, ...)

Arguments

Details

Julia Equivalent: IAI.predict_hazard

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::predict_hazard(lnr, X)

Return the fitted hazard coefficient estimate made by a survival learner for each point in the features.

Description

A higher hazard coefficient estimate corresponds to a smaller predicted survival time.

Usage

## S3 method for class 'survival_learner'
predict_hazard(obj, X, ...)

Arguments

Details

Julia Equivalent: IAI.predict_hazard

IAI Compatibility

Requires IAI version 1.2 or higher.

Examples

## Not run: iai::predict_hazard(lnr, X)

Generic function for returning the outcomes predicted by a model under each treatment

Description

Generic function for returning the outcomes predicted by a model under each treatment

Usage

predict_outcomes(obj, ...)

Arguments

Return the predicted outcome for each treatment made by a policy learner for each point in the features

Description

Julia Equivalent: IAI.predict_outcomes

Usage

## S3 method for class 'policy_learner'
predict_outcomes(obj, X, rewards, ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher

Examples

## Not run: iai::predict_outcomes(lnr, X, rewards)

Return the predicted outcome for each treatment made by a prescription learner for each point in the features

Description

Julia Equivalent: IAI.predict_outcomes

Usage

## S3 method for class 'prescription_learner'
predict_outcomes(obj, X, ...)

Arguments

Examples

## Not run: iai::predict_outcomes(lnr, X)

Generic function for returning the probabilities of class membership predicted by a model

Description

Generic function for returning the probabilities of class membership predicted by a model

Usage

predict_proba(obj, ...)

Arguments

Return the probabilities of class membership predicted by a classification learner for each point in the features

Description

Julia Equivalent: IAI.predict_proba

Usage

## S3 method for class 'classification_learner'
predict_proba(obj, X, ...)

Arguments

Examples

## Not run: iai::predict_proba(lnr, X)

Return the probabilities of class membership predicted by a multi-task classification learner for each point in the features

Description

Julia Equivalent: IAI.predict_proba and IAI.predict_proba

Usage

## S3 method for class 'classification_multi_learner'
predict_proba(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::predict_proba(lnr, X)

Return the probabilities of class membership predicted by a glmnetcv_classifier learner for each point in the features

Description

Julia Equivalent: IAI.predict_proba

Usage

## S3 method for class 'glmnetcv_classifier'
predict_proba(obj, X, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::predict_proba(lnr, X)

Generic function for returning the counterfactual rewards estimated by a model under each treatment

Description

Generic function for returning the counterfactual rewards estimated by a model under each treatment

Usage

predict_reward(obj, ...)

Arguments

Return counterfactual rewards estimated by a categorical reward estimator for each observation in the supplied data and predictions

Description

Julia Equivalent: IAI.predict_reward

Usage

## S3 method for class 'categorical_reward_estimator'
predict_reward(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::predict_reward(lnr, X, treatments, outcomes, predictions)

Return counterfactual rewards estimated by a numeric reward estimator for each observation in the supplied data and predictions

Description

Julia Equivalent: IAI.predict_reward

Usage

## S3 method for class 'numeric_reward_estimator'
predict_reward(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::predict_reward(lnr, X, treatments, outcomes, predictions)

Calculate SHAP values for all points in the features using the learner

Description

Julia Equivalent: IAI.predict_shap

Usage

predict_shap(lnr, X)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::predict_shap(lnr, X)

Return the estimated quality of each treatment in the trained model of the learner for each point in the features

Description

Julia Equivalent: IAI.predict_treatment_outcome

Usage

predict_treatment_outcome(lnr, X)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::predict_treatment_outcome(lnr, X)

Return the standard error for the estimated quality of each treatment in the trained model of the learner for each point in the features

Description

Julia Equivalent: IAI.predict_treatment_outcome_standard_error

Usage

predict_treatment_outcome_standard_error(lnr, X)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::predict_treatment_outcome_standard_error(lnr, X)

Return the treatments in ranked order of effectiveness for each point in the features

Description

Julia Equivalent: IAI.predict_treatment_rank

Usage

predict_treatment_rank(lnr, X)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::predict_treatment_rank(lnr, X)

Print the decision path through the learner for each sample in the features

Description

Julia Equivalent: IAI.print_path

Usage

print_path(lnr, X, ...)

Arguments

Examples

## Not run: 
iai::print_path(lnr, X)
iai::print_path(lnr, X, 1)

## End(Not run)

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

Description

Julia Equivalent: IAI.prune_trees!

Usage

prune_trees(lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::prune_trees(lnr, ...)

Generic function for constructing an interactive questionnaire

Description

Julia Equivalent: IAI.Questionnaire

Usage

questionnaire(obj, ...)

Arguments

Specify an interactive questionnaire of an Optimal Feature Selection learner

Description

Julia Equivalent: IAI.Questionnaire

Usage

## S3 method for class 'optimal_feature_selection_learner'
questionnaire(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::questionnaire(lnr)

Specify an interactive questionnaire of a tree learner

Description

Julia Equivalent: IAI.Questionnaire

Usage

## S3 method for class 'tree_learner'
questionnaire(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::questionnaire(lnr)

Learner for conducting random imputation

Description

Julia Equivalent: IAI.RandImputationLearner

Usage

rand_imputation_learner(...)

Arguments

Examples

## Not run: lnr <- iai::rand_imputation_learner()

Learner for training random forests for classification problems

Description

Julia Equivalent: IAI.RandomForestClassifier

Usage

random_forest_classifier(...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::random_forest_classifier()

Learner for training random forests for regression problems

Description

Julia Equivalent: IAI.RandomForestRegressor

Usage

random_forest_regressor(...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::random_forest_regressor()

Learner for training random forests for survival problems

Description

Julia Equivalent: IAI.RandomForestSurvivalLearner

Usage

random_forest_survival_learner(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::random_forest_survival_learner()

Read in a learner or grid saved in JSON format

Description

Julia Equivalent: IAI.read_json

Usage

read_json(filename)

Arguments

Examples

## Not run: obj <- iai::read_json("out.json")

Refit the models in the leaves of a trained learner using the supplied data

Description

Julia Equivalent: IAI.refit_leaves!

Usage

refit_leaves(lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::refit_leaves(lnr, ...)

Release any IAI license held by the current session.

Description

Julia Equivalent: IAI.release_license

Usage

release_license()

IAI Compatibility

Requires IAI version 3.1 or higher.

Examples

## Not run: iai::release_license()

Reset the predicted probability displayed to be that of the predicted label when visualizing a learner

Description

Julia Equivalent: IAI.reset_display_label!

Usage

reset_display_label(lnr)

Arguments

Examples

## Not run: iai::reset_display_label(lnr)

Resume training from a checkpoint file

Description

Julia Equivalent: IAI.resume_from_checkpoint

Usage

resume_from_checkpoint(checkpoint_file)

Arguments

IAI Compatibility

Requires IAI version 3.1 or higher.

Examples

## Not run: obj <- iai::resume_from_checkpoint("checkpoint.json")

Learner for conducting reward estimation with categorical treatments

Description

This function was deprecated and renamed to categorical_reward_estimator() in iai 1.4.0. This is for consistency with the IAI v2.1.0 Julia release.

Usage

reward_estimator(...)

Arguments

Details

This deprecation is no longer supported as of the IAI v3 release.

IAI Compatibility

Requires IAI version 2.2 or lower.

Examples

## Not run: lnr <- iai::reward_estimator()

Generic function for constructing an ROC curve

Description

Julia Equivalent: IAI.ROCCurve

Usage

roc_curve(obj, ...)

Arguments

Construct an ROC curve using a trained classification learner on the given data

Description

Julia Equivalent: IAI.ROCCurve

Usage

## S3 method for class 'classification_learner'
roc_curve(obj, X, y, ...)

Arguments

Examples

## Not run: iai::roc_curve(lnr, X, y)

Construct an ROC curve using a trained multi-task classification learner on the given data

Description

Julia Equivalent: IAI.ROCCurve and IAI.ROCCurve

Usage

## S3 method for class 'classification_multi_learner'
roc_curve(obj, X, y, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::roc_curve(lnr, X, y)

Construct an ROC curve from predicted probabilities and true labels

Description

Julia Equivalent: IAI.ROCCurve

Usage

## Default S3 method:
roc_curve(obj, y, positive_label = stop("`positive_label` is required"), ...)

Arguments

IAI Compatibility

Requires IAI version 2.0 or higher.

Examples

## Not run: iai::roc_curve(probs, y, positive_label=positive_label)

Construct an ROC curve using a trained glmnetcv_classifier on the given data

Description

Julia Equivalent: IAI.ROCCurve

Usage

## S3 method for class 'glmnetcv_classifier'
roc_curve(obj, X, y, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: iai::roc_curve(lnr, X, y)

Generic function for calculating scores

Description

Generic function for calculating scores

Usage

score(obj, ...)

Arguments

Calculate the scores for a categorical reward estimator on the given data

Description

Julia Equivalent: IAI.score

Usage

## S3 method for class 'categorical_reward_estimator'
score(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::score(lnr, X, treatments, outcomes)

Calculate the score for a set of predictions on the given data

Description

Julia Equivalent: IAI.score

Usage

## Default S3 method:
score(obj, predictions, truths, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::score("regression", y_pred, y_true, criterion="mse")

Calculate the score for a GLMNet learner on the given data

Description

Julia Equivalent: IAI.score

Usage

## S3 method for class 'glmnetcv_learner'
score(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::score(lnr, X, y, fit_index=1)

Calculate the scores for a numeric reward estimator on the given data

Description

Julia Equivalent: IAI.score

Usage

## S3 method for class 'numeric_reward_estimator'
score(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::score(lnr, X, treatments, outcomes)

Calculate the score for an Optimal Feature Selection learner on the given data

Description

Julia Equivalent: IAI.score

Usage

## S3 method for class 'optimal_feature_selection_learner'
score(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::score(lnr, X, y, fit_index=1)

Calculate the score for a model on the given data

Description

Julia Equivalent: IAI.score

Usage

## S3 method for class 'supervised_learner'
score(obj, X, ...)

Arguments

Examples

## Not run: iai::score(lnr, X, y)

Calculate the score for a multi-task model on the given data

Description

Julia Equivalent: IAI.score and IAI.score

Usage

## S3 method for class 'supervised_multi_learner'
score(obj, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.2 or higher.

Examples

## Not run: iai::score(lnr, X, y)

Show the probability of a specified label when visualizing a learner

Description

Julia Equivalent: IAI.set_display_label!

Usage

set_display_label(lnr, display_label)

Arguments

Examples

## Not run: iai::set_display_label(lnr, "A")

Set the random seed in Julia

Description

Julia Equivalent: Random.seed!

Usage

set_julia_seed(seed)

Arguments

Examples

## Not run: iai::set_julia_seed(1)

Set all supplied parameters on a learner

Description

Julia Equivalent: IAI.set_params!

Usage

set_params(lnr, ...)

Arguments

Examples

## Not run: iai::set_params(lnr, random_seed = 1)

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.

Description

Julia Equivalent: IAI.set_reward_kernel_bandwidth!

Usage

set_reward_kernel_bandwidth(lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::set_reward_kernel_bandwidth(lnr, ...)

Sets a global rich output parameter

Description

Julia Equivalent: IAI.set_rich_output_param!

Usage

set_rich_output_param(key, value)

Arguments

Examples

## Not run: iai::set_rich_output_param("simple_layout", TRUE)

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.

Description

Julia Equivalent: IAI.set_threshold!

Usage

set_threshold(lnr, label, threshold, ...)

Arguments

Examples

## Not run: iai::set_threshold(lnr, "A", 0.4)

Generic function for showing interactive visualization in browser

Description

Generic function for showing interactive visualization in browser

Usage

show_in_browser(obj, ...)

Arguments

Show interactive visualization of an object in the default browser

Description

Julia Equivalent: IAI.show_in_browser

Usage

## S3 method for class 'abstract_visualization'
show_in_browser(obj, ...)

Arguments

Examples

## Not run: iai::show_in_browser(lnr)

Show interactive visualization of a roc_curve in the default browser

Description

Julia Equivalent: IAI.show_in_browser

Usage

## S3 method for class 'roc_curve'
show_in_browser(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::show_in_browser(curve)

Show interactive tree visualization of a tree learner in the default browser

Description

Julia Equivalent: IAI.show_in_browser

Usage

## S3 method for class 'tree_learner'
show_in_browser(obj, ...)

Arguments

IAI Compatibility

Showing a grid search requires IAI version 2.0 or higher.

Examples

## Not run: iai::show_in_browser(lnr)

Generic function for showing interactive questionnaire in browser

Description

Generic function for showing interactive questionnaire in browser

Usage

show_questionnaire(obj, ...)

Arguments

Show an interactive questionnaire based on an Optimal Feature Selection learner in default browser

Description

Julia Equivalent: IAI.show_questionnaire

Usage

## S3 method for class 'optimal_feature_selection_learner'
show_questionnaire(obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::show_questionnaire(lnr)

Show an interactive questionnaire based on a tree learner in default browser

Description

Julia Equivalent: IAI.show_questionnaire

Usage

## S3 method for class 'tree_learner'
show_questionnaire(obj, ...)

Arguments

IAI Compatibility

Showing a grid search requires IAI version 2.0 or higher.

Examples

## Not run: iai::show_questionnaire(lnr)

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

Description

Refer to the documentation on tree stability for more information.

Usage

similarity_comparison(lnr, new_lnr, deviations)

Arguments

Details

Julia Equivalent: IAI.SimilarityComparison

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::similarity_comparison(lnr, new_lnr, deviations)

Learner for conducting heuristic k-NN imputation

Description

Julia Equivalent: IAI.SingleKNNImputationLearner

Usage

single_knn_imputation_learner(...)

Arguments

Examples

## Not run: lnr <- iai::single_knn_imputation_learner()

Split the data into training and test datasets

Description

Julia Equivalent: IAI.split_data

Usage

split_data(task, X, ...)

Arguments

Examples

## Not run: 
X <- iris[, 1:4]
y <- iris$Species
split <- iai::split_data("classification", X, y, train_proportion = 0.75)
train_X <- split$train$X
train_y <- split$train$y
test_X <- split$test$X
test_y <- split$test$y

## End(Not run)

Conduct a stability analysis of the trees in a tree learner

Description

Refer to the documentation on tree stability for more information.

Usage

stability_analysis(lnr, ...)

Arguments

Details

Julia Equivalent: IAI.StabilityAnalysis

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::stability_analysis(lnr, ...)

Impute missing values in a dataframe using a fitted imputation model

Description

Julia Equivalent: IAI.transform

Usage

transform(lnr, X)

Arguments

Examples

## Not run: iai::transform(lnr, X)

Transform features with a trained imputation learner and create adaptive indicator features to encode the missing pattern

Description

Julia Equivalent: IAI.transform_and_expand

Usage

transform_and_expand(lnr, X, ...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: lnr <- iai::transform_and_expand(lnr, X, type = "finite")

Specify an interactive tree visualization of a tree learner

Description

Julia Equivalent: IAI.TreePlot

Usage

tree_plot(lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::tree_plot(lnr)

Conduct the reward kernel bandwidth tuning procedure for a range of starting bandwidths and return the final tuned values.

Description

Julia Equivalent: IAI.tune_reward_kernel_bandwidth

Usage

tune_reward_kernel_bandwidth(lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::tune_reward_kernel_bandwidth(lnr, ...)

Generic function for calculating variable importance

Description

Generic function for calculating variable importance

Usage

variable_importance(obj, ...)

Arguments

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.

Description

Julia Equivalent: IAI.variable_importance

Usage

## S3 method for class 'learner'
variable_importance(obj, ...)

Arguments

Examples

## Not run: iai::variable_importance(lnr, ...)

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.

Description

Julia Equivalent: IAI.variable_importance

Usage

## S3 method for class 'optimal_feature_selection_learner'
variable_importance(obj, fit_index = NULL, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::variable_importance(lnr, ...)

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.

Description

Julia Equivalent: IAI.variable_importance

Usage

## S3 method for class 'tree_learner'
variable_importance(obj, ...)

Arguments

Examples

## Not run: iai::variable_importance(lnr, ...)

Calculate similarity between the final tree in a tree learner with all trees in new tree learner using variable importance scores.

Description

Julia Equivalent: IAI.variable_importance_similarity

Usage

variable_importance_similarity(lnr, new_lnr, ...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: iai::variable_importance_similarity(lnr, new_lnr)

Write the internal booster saved in the learner to file

Description

Julia Equivalent: IAI.write_booster

Usage

write_booster(filename, lnr)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::write_booster(file.path(tempdir(), "out.json"), lnr)

Output a learner in .dot format

Description

Julia Equivalent: IAI.write_dot

Usage

write_dot(filename, lnr, ...)

Arguments

Examples

## Not run: iai::write_dot(file.path(tempdir(), "tree.dot"), lnr)

Generic function for writing interactive visualization to file

Description

Generic function for writing interactive visualization to file

Usage

write_html(filename, obj, ...)

Arguments

Output an object as an interactive browser visualization in HTML format

Description

Julia Equivalent: IAI.write_html

Usage

## S3 method for class 'abstract_visualization'
write_html(filename, obj, ...)

Arguments

Examples

## Not run: iai::write_html(file.path(tempdir(), "out.html"), lnr)

Output an ROC curve as an interactive browser visualization in HTML format

Description

Julia Equivalent: IAI.write_html

Usage

## S3 method for class 'roc_curve'
write_html(filename, obj, ...)

Arguments

IAI Compatibility

Requires IAI version 1.1 or higher.

Examples

## Not run: iai::write_html(file.path(tempdir(), "roc.html"), lnr)

Output a tree learner as an interactive browser visualization in HTML format

Description

Julia Equivalent: IAI.write_html

Usage

## S3 method for class 'tree_learner'
write_html(filename, obj, ...)

Arguments

IAI Compatibility

Outputting a grid search requires IAI version 2.0 or higher.

Examples

## Not run: iai::write_html(file.path(tempdir(), "tree.html"), lnr)

Output a learner or grid in JSON format

Description

Julia Equivalent: IAI.write_json

Usage

write_json(filename, obj, ...)

Arguments

Examples

## Not run: iai::write_json(file.path(tempdir(), "out.json"), obj)

Output a learner as a PDF image

Description

Before using this function, either run load_graphviz or ensure that Graphviz is installed and on the system PATH

Usage

write_pdf(filename, lnr, ...)

Arguments

Details

Julia Equivalent: IAI.write_pdf

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::write_pdf(file.path(tempdir(), "tree.pdf"), lnr)

Output a learner as a PNG image

Description

Before using this function, either run load_graphviz or ensure that Graphviz is installed and on the system PATH

Usage

write_png(filename, lnr, ...)

Arguments

Details

Julia Equivalent: IAI.write_png

Examples

## Not run: iai::write_png(file.path(tempdir(), "tree.png"), lnr)

Generic function for writing interactive questionnaire to file

Description

Generic function for writing interactive questionnaire to file

Usage

write_questionnaire(filename, obj, ...)

Arguments

Output an Optimal Feature Selection learner as an interactive questionnaire in HTML format

Description

Julia Equivalent: IAI.write_questionnaire

Usage

## S3 method for class 'optimal_feature_selection_learner'
write_questionnaire(filename, obj, ...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::write_questionnaire(file.path(tempdir(), "questionnaire.html"), lnr)

Output a tree learner as an interactive questionnaire in HTML format

Description

Julia Equivalent: IAI.write_questionnaire

Usage

## S3 method for class 'tree_learner'
write_questionnaire(filename, obj, ...)

Arguments

IAI Compatibility

Outputting a grid search requires IAI version 2.0 or higher.

Examples

## Not run: iai::write_questionnaire(file.path(tempdir(), "questionnaire.html"), lnr)

Output a learner as a SVG image

Description

Before using this function, either run load_graphviz or ensure that Graphviz is installed and on the system PATH

Usage

write_svg(filename, lnr, ...)

Arguments

Details

Julia Equivalent: IAI.write_svg

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: iai::write_svg(file.path(tempdir(), "tree.svg"), lnr)

Learner for training XGBoost models for classification problems

Description

Julia Equivalent: IAI.XGBoostClassifier

Usage

xgboost_classifier(...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::xgboost_classifier()

Learner for training XGBoost models for regression problems

Description

Julia Equivalent: IAI.XGBoostRegressor

Usage

xgboost_regressor(...)

Arguments

IAI Compatibility

Requires IAI version 2.1 or higher.

Examples

## Not run: lnr <- iai::xgboost_regressor()

Learner for training XGBoost models for survival problems

Description

Julia Equivalent: IAI.XGBoostSurvivalLearner

Usage

xgboost_survival_learner(...)

Arguments

IAI Compatibility

Requires IAI version 2.2 or higher.

Examples

## Not run: lnr <- iai::xgboost_survival_learner()

Learner for conducting zero-imputation

Description

Julia Equivalent: IAI.ZeroImputationLearner

Usage

zero_imputation_learner(...)

Arguments

IAI Compatibility

Requires IAI version 3.0 or higher.

Examples

## Not run: lnr <- iai::zero_imputation_learner()