How to use the verticapy.learn.ensemble.RandomForestRegressor function in verticapy

del parameters[0]
		parameters += ["class_weights=" + info[1].split("class_weights=")[1].split("'")[1]]
	elif (model_type != "svd"):
		parameters = info[1].split(",")
	if (model_type != "svd"):
		parameters = [item.split("=") for item in parameters]
		parameters_dict = {}
		for item in parameters:
			parameters_dict[item[0]] = item[1]
	info = info[0]
	for elem in parameters_dict:
		if type(parameters_dict[elem]) == str:
			parameters_dict[elem] = parameters_dict[elem].replace("'", "")
	if (model_type == "rf_regressor"):
		from verticapy.learn.ensemble import RandomForestRegressor
		model = RandomForestRegressor(name, cursor, int(parameters_dict['ntree']), int(parameters_dict['mtry']), int(parameters_dict['max_breadth']), float(parameters_dict['sampling_size']), int(parameters_dict['max_depth']), int(parameters_dict['min_leaf_size']), float(parameters_dict['min_info_gain']), int(parameters_dict['nbins']))
	elif (model_type == "rf_classifier"):
		from verticapy.learn.ensemble import RandomForestClassifier
		model = RandomForestClassifier(name, cursor, int(parameters_dict['ntree']), int(parameters_dict['mtry']), int(parameters_dict['max_breadth']), float(parameters_dict['sampling_size']), int(parameters_dict['max_depth']), int(parameters_dict['min_leaf_size']), float(parameters_dict['min_info_gain']), int(parameters_dict['nbins']))
	elif (model_type == "logistic_reg"):
		from verticapy.learn.linear_model import LogisticRegression
		model = LogisticRegression(name, cursor, parameters_dict['regularization'], float(parameters_dict['epsilon']), float(parameters_dict['lambda']), int(parameters_dict['max_iterations']), parameters_dict['optimizer'], float(parameters_dict['alpha']))
	elif (model_type == "linear_reg"):
		from verticapy.learn.linear_model import ElasticNet
		model = ElasticNet(name, cursor, parameters_dict['regularization'], float(parameters_dict['epsilon']), float(parameters_dict['lambda']), int(parameters_dict['max_iterations']), parameters_dict['optimizer'], float(parameters_dict['alpha']))
	elif (model_type == "naive_bayes"):
		from verticapy.learn.naive_bayes import MultinomialNB
		model = MultinomialNB(name, cursor, float(parameters_dict['alpha']))
	elif (model_type == "svm_regressor"):
		from verticapy.learn.svm import LinearSVR
		model = LinearSVR(name, cursor, float(parameters_dict['epsilon']), float(parameters_dict['C']), True, float(parameters_dict['intercept_scaling']), parameters_dict['intercept_mode'], float(parameters_dict['error_tolerance']), int(parameters_dict['max_iterations']))
	elif (model_type == "svm_classifier"):

The maximum number of leaf nodes a tree in the forest can have, an integer 
	between 1 and 1e9, inclusive. 
max_depth: int, optional
	The maximum depth for growing each tree, an integer between 1 and 100, inclusive.
min_samples_leaf: int, optional
	The minimum number of samples each branch must have after splitting a node, an 
	integer between 1 and 1e6, inclusive. A split that causes fewer remaining samples 
	is discarded. 
min_info_gain: float, optional
	The minimum threshold for including a split, a float between 0.0 and 1.0, inclusive. 
	A split with information gain less than this threshold is discarded.
nbins: int, optional 
	The number of bins to use for continuous features, an integer between 2 and 1000, 
	inclusive.
	"""
	return RandomForestRegressor(name = name, 
								 cursor = cursor, 
								 n_estimators = 1, 
								 max_features = max_features, 
								 max_leaf_nodes = max_leaf_nodes,
								 sample = 1.0,
								 max_depth = max_depth,
								 min_samples_leaf = min_samples_leaf,
								 min_info_gain = min_info_gain,
								 nbins = nbins)
#---#

def DummyTreeRegressor(name: str, 
					   cursor = None):
	"""
---------------------------------------------------------------------------
Dummy Tree Regressor. This regressor learns by heart the training data. 
 => very depth RandomForestRegressor of one tree using all the data.

Parameters
----------
name: str
	Name of the the model. The model will be stored in the DB.
cursor: DBcursor, optional
	Vertica DB cursor. 
	"""
	return RandomForestRegressor(name = name, 
								 cursor = cursor, 
								 n_estimators = 1, 
								 max_features = "max", 
								 max_leaf_nodes = 1e9,
								 sample = 1.0,
								 max_depth = 100,
								 min_samples_leaf = 1,
								 min_info_gain = 0.0,
								 nbins = 1000)