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Estimator API

Public 1.0.0 estimators cover gradient boosting, single decision trees, random forests, ExtraTrees, CatBoost-like numeric estimators, IsolationForest, and learning-to-rank entries. They follow the sklearn-style protocol, so normal training, prediction, scoring, and parameter search can use one method set.

Public names include:

  • GradientBoostingRegressor
  • GradientBoostingClassifier
  • MPSBoostRegressor
  • MPSBoostClassifier
  • DecisionTreeRegressor
  • DecisionTreeClassifier
  • RandomForestRegressor
  • RandomForestClassifier
  • ExtraTreesRegressor
  • ExtraTreesClassifier
  • ExtraTreeRegressor
  • ExtraTreeClassifier
  • CatBoostRegressor
  • CatBoostClassifier
  • IsolationForest
  • MPSIsolationForest
  • LearningToRankRegressor

These estimators follow these sklearn-style entries:

  • fit
  • predict
  • predict_proba, for classifiers
  • score
  • get_params
  • set_params

Advanced regression objectives are selected through loss:

  • loss="squared_error"
  • loss="quantile"
  • loss="poisson"
  • loss="tweedie"

Classifiers additionally support probability outputs. Multiclass defaults to the native softmax path when available; OvR is only an explicit compatibility strategy or staged fallback. device="auto" chooses CPU or MPS according to environment and workload, and the training summary records the actual backend so users do not need to guess which path ran.