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default.estimator

flamlize_estimator

def flamlize_estimator(super_class, name: str, task: str, alternatives=None)

Enhance an estimator class with flaml's data-dependent default hyperparameter settings.

Example:

import sklearn.ensemble as ensemble
RandomForestRegressor = flamlize_estimator(
    ensemble.RandomForestRegressor, "rf", "regression"
)

Arguments:

  • super_class - an scikit-learn compatible estimator class.
  • name - a str of the estimator's name.
  • task - a str of the task type.
  • alternatives - (Optional) a list for alternative estimator names. For example, [("max_depth", 0, "xgboost")] means if the "max_depth" is set to 0 in the constructor, then look for the learned defaults for estimator "xgboost".