DecisionTreeClassifier¶

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DecisionTreeClassifier(name: str,
                       max_features = "auto",
                       max_leaf_nodes: int = 1e9,
                       max_depth: int = 5,
                       min_samples_leaf: int = 1,
                       min_info_gain: float = 0.0,
                       nbins: int = 32)

A DecisionTreeClassifier made of a single tree.

Parameters¶

Name	Type	Optional	Description
name	str	❌	Name of the model to be stored in the database.
max_features	str	✓	The number of randomly chosen features from which to pick the best feature to split on a given tree node. This 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.
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 produces 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. Splits that gain less information than this threshold are discarded.
nbins	int	✓	The number of bins to use for continuous features: an integer between 2 and 1000, inclusive.

Attributes¶

After the object is created, all parameters become attributes. Additional attributes will be created when fitting the model:

Name	Type	Description
classes_	list	List of all the response classes.
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
classification_report / report	Computes a classification report using various metrics to evaluate the model (AUC, accuracy, PRC AUC, F1...). For multiclass classification, it will consider each category as positive and switch to the next one during the computation.
confusion_matrix	Computes the model confusion matrix.
contour	Draws the model's contour plot.
cutoff_curve	Draws the model's Cutoff curve.
deploySQL	Returns the SQL code needed to deploy the model.
drop	Drops the model from the Vertica database.
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.
lift_chart	Draws the model's lift chart.
plot_tree	Draws the input tree (requires the graphviz module).
prc_curve	Draws the model's PRC curve.
predict	Predicts using the input relation.
predict_proba	Returns the model's probabilities using the input relation.
roc_curve	Draws the model's ROC curve.
score	Computes the model's 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.tree import DecisionTreeClassifier
model = DecisionTreeClassifier(name = "public.rf_titanic",
                               max_features = "auto",
                               max_leaf_nodes = 32,
                               max_depth = 3,
                               min_samples_leaf = 5,
                               min_info_gain = 0.0,
                               nbins = 32)
display(model)

<RandomForestClassifier>