iai

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

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

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

Convert a vector of values to IAI mixed data format

Learner for conducting reward estimation with categorical treatments

Return an unfitted copy of a learner with the same parameters

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

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.

Delete a global rich output parameter

Learner that estimates equal propensity for all treatments.

Fits a model to the training data

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

Fit a reward estimation model on features, treatments and outcomes and return predicted counterfactual rewards for each observation, as well as the score of the internal outcome estimator.

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

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

Return the best parameter combination from a grid

Return the predicted label at a node of a tree

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

Get the depth of a node of a tree

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

Return a summary of the results from the grid search

Return a summary of the results from the grid search

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

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

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

Return the number of nodes in a trained learner

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

Return the value of all parameters on a learner

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

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

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

Return the constant term in the prediction in the trained learner

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

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

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

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

Return the current global rich output parameter settings

Extract the underlying data from an ROC curve (as returned by roc_curve)

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

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

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

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

Return the survival curve at a node of a tree

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

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

Return the predicted hazard ratio at a node of a tree

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

Learner for training GLMNet models for regression problems

Controls grid search over parameter combinations

Initialize Julia and the IAI package.

Generic learner for imputing missing values

Impute missing values using either a specified method or through validation

Impute missing values using cross validation

Download and install Julia automatically.

Download and install the IAI system image automatically.

Check if a node of a tree applies a categoric split

Check if a node of a tree applies a hyperplane split

Check if a node of a tree is a leaf

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

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

Check if a node of a tree applies a ordinal split

Check if a node of a tree applies a parallel split

Learner for conducting mean imputation

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

Generic function for constructing an interactive questionnaire using multiple tree learners

Construct an interactive questionnaire using multiple tree learners as specified by questions

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

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

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

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

Learner for conducting reward estimation with numeric treatments

Learner for conducting optimal k-NN imputation

Learner for conducting optimal SVM imputation

Learner for conducting optimal tree-based imputation

Learner for conducting Optimal Feature Selection on classification problems

Learner for conducting Optimal Feature Selection on regression problems

Learner for training Optimal Classification Trees

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

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

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

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

Learner for training Optimal Regression Trees

Learner for training Optimal Survival Trees

Learner for training Optimal Survival Trees

Return the predictions made by the model for each point in the features

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

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

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

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

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

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

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

Specify an interactive questionnaire of a tree learner

Learner for conducting random imputation

Learner for training random forests for classification problems

Learner for training random forests for regression problems

Read in a learner or grid saved in JSON format

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

Learner for conducting reward estimation with categorical treatments

Generic function for constructing an ROC curve

Construct an ROC curve from predicted probabilities and true labels

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

Calculate the score for a model on the given data

Show the probability of a specified label when visualizing a learner

Set the random seed in Julia

Set all supplied parameters on a learner

Sets a global rich output parameter

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.

Show interactive visualization of an object (such as a learner or curve) in the default browser

Show an interactive questionnaire based on a learner in default browser

Learner for conducting heuristic k-NN imputation

Split the data into training and test datasets

Impute missing values in a dataframe using a fitted imputation model

Specify an interactive tree visualization of a tree learner

Generate a ranking of the variables in the learner according to their importance during training. The results are normalized so that they sum to one.

Write the internal booster saved in the learner to file

Output a learner in .dot format

Output a learner as an interactive browser visualization in HTML format

Output a learner or grid in JSON format

Output a learner as a PDF image

Output a learner as a PNG image

Output a learner as an interactive questionnaire in HTML format

Output a learner as a SVG image

Learner for training XGBoost models for classification problems

Learner for training XGBoost models for regression problems