API Reference

Abstract type encompassing all random forest learners.

Learner for training random forests for classification problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• shared OptimalTreeLearner parameters
• shared RandomForestLearner parameters

Learner for training random forests for regression problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• regression Learner parameters
• shared OptimalTreeLearner parameters
• regression OptimalTreeLearner parameters
• shared RandomForestLearner parameters

Learner for training random forests for survival problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• survival Learner parameters
• shared OptimalTreeLearner parameters
• survival OptimalTreeLearner parameters
• shared RandomForestLearner parameters

Abstract type encompassing all XGBoost learners.

Learner for training XGBoost models for classification problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• shared XGBoostLearner parameters

Learner for training XGBoost models for regression problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• regression Learner parameters
• shared XGBoostLearner parameters

Learner for training XGBoost models for survival problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• survival Learner parameters
• shared XGBoostLearner parameters

predict_shap(lnr::XGBoostLearner, X::FeatureInput)

Calculate SHAP values for all points in the features X using lnr.

Returns a Dict with the following keys:

• :shap_values: A Matrix of SHAP values for each sample and feature in X. For classification problems, this key contains a Vector of such matrices, one for each target label.
• :expected_value: A Float64 containing the expected value for use in SHAP analysis. For classification problems, this key contains a Vector of such values, one for each target label.
• :features: A DataFrame containing the features X in the appropriate format to pass to SHAP visualization functions.
• :labels: A Vector giving the order of the target labels in the problem, corresponding to the order of the results in :shap_values and :expected_value (for classification problems only)

These calculations are analogous to the following Python code:

import shap
explainer = shap.TreeExplainer(lnr)
shap_values = explainer.shap_values(X)
expected_value = explainer.expected_value

write_booster(filepath, lnr::XGBoostLearner)

Write the internal booster saved in lnr to filepath.

This can be used to open the trained XGBoost model using the XGBoost Python wrapper in order to conduct a SHAP analysis.

If filepath ends in ".json" the booster will be written in JSON format, otherwise an internal XGBoost binary format is used.

Abstract type encompassing all GLMNet learners

Abstract type encompassing all GLMNet learners with cross-validation.

get_num_fits(lnr::GLMNetCVLearner)

Returns the number of fits along the path in lnr.

predict(lnr::GLMNetCVLearner, X::FeatureInput)

Returns the prediction made by the best fit on the path for lnr for each point in the data X.

predict(lnr::GLMNetCVLearner, X::FeatureInput; fit_index::Integer)

Returns the prediction made by the fit at fit_index on the path for lnr for each point in the data X.

score(lnr::GLMNetCVLearner, X::FeatureInput, y::TargetInput...;
      keyword_arguments...)

Calculates the score for the best fit on the path for lnr on data X and y.

score(lnr::GLMNetCVLearner, X::FeatureInput, y::TargetInput...;
      fit_index::Integer, keyword_arguments...)

Calculates the score for the fit at fit_index on the path for lnr on data X and y.

get_prediction_weights(lnr::GLMNetCVLearner)

Return the weights for each feature in the prediction made by the best fit on the path in lnr.

get_prediction_weights(lnr::GLMNetCVLearner; fit_index::Integer)

Return the weights for each feature in the prediction made by the fit at fit_index on the path in lnr.

get_prediction_constant(lnr::GLMNetCVLearner)

Return the constant term in the prediction made by the best fit on the path in lnr.

get_prediction_constant(lnr::GLMNetCVLearner; fit_index::Integer)

Return the constant term in the prediction made by the fit at fit_index on the path in lnr.

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

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• regression Learner parameters
• shared GLMNetCVLearner parameters

predict_proba(lnr::GLMNetCVClassifier, X::FeatureInput)

Returns the probabilities of class membership predicted by the best fit on the path for lnr for each point in the data X.

predict_proba(lnr::GLMNetCVClassifier, X::FeatureInput; fit_index::Integer)

Returns the probabilities of class membership predicted by the fit at fit_index on the path for lnr for each point in the data X.

ROCCurve(lnr::GLMNetCVClassifier, X::FeatureInput, y::AbstractVector;
         positive_label)

Construct a ROCCurve using the best fit on the path for lnr on the features X and labels y, treating positive_label as the positive label.

ROCCurve(lnr::GLMNetCVClassifier, X::FeatureInput, y::AbstractVector;
         positive_label, fit_index::Integer)

Construct a ROCCurve using the fit at fit_index on the path for lnr on the features X and labels y, treating positive_label as the positive label.

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

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• regression Learner parameters
• shared GLMNetCVLearner parameters

Learner for training GLMNetCV models for survival problems.

The following parameters are supported (refer to the documentation for each):

• shared Learner parameters
• survival Learner parameters
• shared GLMNetCVLearner parameters

predict_hazard(lnr::GLMNetCVSurvivalLearner, X::FeatureInput)

Returns the fitted hazard coefficient estimate made by the best fit on the path for lnr for each point in the data X. A higher hazard coefficient estimate corresponds to a smaller predicted survival time.

predict_hazard(lnr::GLMNetCVSurvivalLearner, X::FeatureInput;
               fit_index::Integer)

Returns the fitted hazard coefficient estimate made by the fit at fit_index on the path for lnr for each point in the data X. A higher hazard coefficient estimate corresponds to a smaller predicted survival time.

predict_expected_survival_time(lnr::GLMNetCVSurvivalLearner,
                               X::FeatureInput)

Returns the expected survival time according to the best fit on the path for lnr for each point in the data X.

predict_expected_survival_time(lnr::GLMNetCVSurvivalLearner,
                               X::FeatureInput;
                               fit_index::Integer)

Returns the expected survival time according to the fit at fit_index for lnr for each point in the data X.