d6a79fbee9d2d4d93bd68153bc1a0357c6c41a4c,hpsklearn/components.py,,random_forest_regression,#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,785

Before Change

    Out of bag estimation only available if bootstrap=True
    

    bootstrap_oob = hp.choice(_name("bootstrap_oob"),
                              [(True, True),
                               (True, False),
                               (False, False)])

    rval = scope.sklearn_RandomForestRegressor(
        n_estimators=scope.int(hp.quniform(
            _name("n_estimators"),
            1, 50, 1)) if n_estimators is None else n_estimators,
        criterion="mse" if criterion is None else criterion,
        max_features=hp.choice(
            _name("max_features"),
            ["sqrt", "log2",
             None]) if max_features is None else max_features,
        max_depth=max_depth,
        min_samples_split=hp.quniform(
            _name("min_samples_split"),
            1, 10, 1) if min_samples_split is None else min_samples_split,
        min_samples_leaf=hp.quniform(
            _name("min_samples_leaf"),
            1, 5, 1) if min_samples_leaf is None else min_samples_leaf,
        bootstrap=bootstrap_oob[0] if bootstrap is None else bootstrap,
        oob_score=bootstrap_oob[1] if oob_score is None else oob_score,
        n_jobs=n_jobs,
        random_state=_random_state(_name("rstate"), random_state),
        verbose=verbose,

After Change

        criterion=criterion,
        max_features=(_trees_max_features(_name("max_features")) 
                      if max_features is None else max_features),
        max_depth=(_trees_max_depth(_name("max_depth")) 
                   if max_depth is None else max_depth),
        min_samples_split=(_trees_min_samples_split(_name("min_samples_split")) 
                           if min_samples_split is None else min_samples_split),

Instances