def _fit_random_forest(X_train, y_train,
obj_func_name = "mse",
sample_weight = None,
model_params = None,
log_target_reg = False):
if model_params is None:
model_params = {}
# checking if obj_func is correct
_check_obj_func_name(obj_func_name, "random_forest")
if "criterion" in model_params.keys():
assert obj_func_name == model_params["criterion"], "use the obj_func_name argument instead of setting criterion in model_params"
model_params["criterion"] = obj_func_name
model = RandomForestRegressor(**model_params)
if log_target_reg:
model = _log_target_regressor(model)
model.fit(X = X_train, y = y_train, sample_weight = sample_weight)
if log_target_reg:
if hasattr(model.regressor_, "oob_prediction_"):
oob_pred = model.regressor_.oob_prediction_
# must convert back to original units
oob_pred = model.inverse_func(oob_pred)
else:
oob_pred = None
model.oob_prediction_ = oob_pred
model.feature_importances_ = model.regressor_.feature_importances_
return model
