def deserialize_random_forest_regressor(model_dict):
model = RandomForestRegressor(**model_dict['params'])
estimators = [
deserialize_decision_tree_regressor(decision_tree)
for decision_tree in model_dict['estimators_']
]
model.estimators_ = np.array(estimators)
model.n_features_ = model_dict['n_features_']
model.n_outputs_ = model_dict['n_outputs_']
model.max_depth = model_dict['max_depth']
model.min_samples_split = model_dict['min_samples_split']
model.min_samples_leaf = model_dict['min_samples_leaf']
model.min_weight_fraction_leaf = model_dict['min_weight_fraction_leaf']
model.max_features = model_dict['max_features']
model.max_leaf_nodes = model_dict['max_leaf_nodes']
model.min_impurity_decrease = model_dict['min_impurity_decrease']
model.min_impurity_split = model_dict['min_impurity_split']
if 'oob_score_' in model_dict:
model.oob_score_ = model_dict['oob_score_']
if 'oob_prediction_' in model_dict:
model.oob_prediction_ = np.array(model_dict['oob_prediction_'])
return model
