def deserialize_random_forest(model_dict):
model = RandomForestClassifier(**model_dict['params'])
estimators = [deserialize_decision_tree(decision_tree) for decision_tree in model_dict['estimators_']]
model.estimators_ = np.array(estimators)
model.classes_ = np.array(model_dict['classes_'])
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_decision_function_' in model_dict:
model.oob_decision_function_ = model_dict['oob_decision_function_']
if isinstance(model_dict['n_classes_'], list):
model.n_classes_ = np.array(model_dict['n_classes_'])
else:
model.n_classes_ = model_dict['n_classes_']
return model
