RandomForestRegressor¶

In [ ]:

RandomForestRegressor(name: str,
                      n_estimators: int = 10,
                      max_features = "auto",
                      max_leaf_nodes: int = 1e9, 
                      sample: float = 0.632,
                      max_depth: int = 5,
                      min_samples_leaf: int = 1,
                      min_info_gain: float = 0.0,
                      nbins: int = 32)

Creates a RandomForestRegressor object using the Vertica RF_REGRESSOR function. It is one of the ensemble learning methods for regression that operates by constructing a multitude of decision trees at training-time and outputting a class with the mode.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
n_estimators	int	✓	The number of trees in the forest, an integer between 1 and 1000, inclusive.
max_features	str	✓	The number of randomly chosen features from which to pick the best feature to split on a given tree node. It can be an integer or one of the two following methods. auto : square root of the total number of predictors. max : number of predictors.
max_leaf_nodes	int	✓	The maximum number of leaf nodes a tree in the forest can have, an integer between 1 and 1e9, inclusive.
sample	float	✓	The portion of the input data set that is randomly picked for training each tree, a float between 0.0 and 1.0, inclusive.
max_depth	int	✓	The maximum depth for growing each tree, an integer between 1 and 100, inclusive.
min_samples_leaf	int	✓	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	✓	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	✓	The number of bins to use for continuous features, an integer between 2 and 1000, inclusive.

Attributes¶

After the object creation, all the parameters become attributes. The model will also create extra attributes when fitting the model:

Name	Type	Description
input_relation	str	Training relation.
X	list	List of the predictors.
y	str	Response column.
test_relation	str	Relation to use to test the model. All model methods are abstractions that simplify the process. The testing relation will be used by the methods to evaluate the model. If empty, the training relation will be used instead. This attribute can be changed at any time.

Methods¶

Name	Description
contour	Draws the model's contour plot.
deploySQL	Returns the SQL code needed to deploy the model.
drop	Drops the model from the Vertica DB.
features_importance	Computes the model features importance using the Gini Index.
fit	Trains the model.
get_attr	Returns the model attribute.
get_params	Returns the model Parameters.
get_tree	Returns a tablesample with all the input tree information.
plot	Draws the Model.
plot_tree	Draws the input tree (requires the graphviz module).
predict	Predicts using the input relation.
regression_report / report	Computes a regression report using multiple metrics to evaluate the model (r2, mse, max error...).
score	Computes the model score.
set_params	Sets the parameters of the model.
to_graphviz	Converts the input tree to a Graphviz tree.
to_memmodel	Converts a specified Vertica model to a memModel model.
to_python	Returns the Python code needed to deploy the model without using built-in Vertica functions.
to_sql	Returns the SQL code needed to deploy the model without using Vertica built-in functions.

Example¶

In [3]:

from verticapy.learn.ensemble import RandomForestRegressor
model = RandomForestRegressor(name = "public.rf_winequality",
                              n_estimators = 20,
                              max_features = "auto",
                              max_leaf_nodes = 32, 
                              sample = 0.7,
                              max_depth = 3,
                              min_samples_leaf = 5,
                              min_info_gain = 0.0,
                              nbins = 32)
display(model)

<RandomForestRegressor>