Module learner 

Nuisance learners: the Regressor / Classifier traits and the built-in implementations.

Structs

GradientBoostingClassifier

Gradient boosting for 0/1 classification (log loss). Defaults match scikit-learn’s GradientBoostingClassifier.

GradientBoostingRegressor

Gradient boosting for regression (squared error). Defaults match scikit-learn’s GradientBoostingRegressor: 100 stages, learning rate 0.1, depth-3 trees.

Lasso

Lasso regression with an unpenalised intercept, fitted by cyclic coordinate descent.

LogisticRegression

L2-penalised logistic regression with an unpenalised intercept, fitted by Newton’s method with backtracking line search.

Ols

Ordinary least squares with an intercept and optional ridge penalty.

RandomForestClassifier

Random forest classifier for 0/1 labels; predict_proba averages the class-1 frequency of the leaves.

RandomForestRegressor

Random forest regressor (squared-error CART trees on bootstrap samples).

Enums

MaxFeatures

Number of features considered at each split.

Traits

Classifier

A classification learner returning P(label = 1 | X), used for propensity scores in models with a binary treatment such as Irm.

Regressor

A regression learner for nuisance functions such as E[Y | X] or E[D | X].