def __init__(self, sig_weight=1., pow_sig=1., pow_bg=1., gap=1., n_estimators=10, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, max_features="auto", bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, min_density=None, compute_importances=None): RandomForestRegressor.__init__(self) # Everything should be set via set_params self.sig_weight = sig_weight self.pow_bg = pow_bg self.pow_sig = pow_sig self.gap = gap
def __init__( self, sc=None, partitions="auto", n_estimators=100, criterion="mse", max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features="auto", max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, ): RandomForestRegressor.__init__( self, n_estimators=n_estimators, criterion=criterion, max_depth=max_depth, min_samples_split=min_samples_split, min_samples_leaf=min_samples_leaf, min_weight_fraction_leaf=min_weight_fraction_leaf, max_features=max_features, max_leaf_nodes=max_leaf_nodes, min_impurity_decrease=min_impurity_decrease, min_impurity_split=min_impurity_split, bootstrap=bootstrap, oob_score=oob_score, n_jobs=n_jobs, random_state=random_state, verbose=verbose, warm_start=warm_start, ) self.sc = sc self.partitions = partitions