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
