Model.get_tree¶

In [ ]:

Model.get_tree(tree_id: int = 0)

Returns a table with all the input tree information.

Parameters¶

Name	Type	Optional	Description
tree_id	int	✓	Unique tree identifier. It is an integer between 0 and n_estimators - 1

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [51]:

from verticapy.learn.ensemble import RandomForestClassifier
model = RandomForestClassifier(name = "public.RF_iris",
                               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)
model.fit("public.iris", 
          ["PetalLengthCm", "PetalWidthCm"], 
          "Species")
model.get_tree(tree_id = 2)

	123 tree_id Integer	123 node_id Integer	123 node_depth Integer	010 is_leaf Boolean	010 is_categorical_split Boolean	Abc split_predictor Varchar(128)	Abc split_value Varchar(65000)	123 weighted_information_gain Float	123 left_child_id Integer	123 right_child_id Integer	Abc prediction Varchar(65000)	123 probability/variance Float
1	2	1	0	❌	❌	petallengthcm	1.921875	0.213174886116063	2	3	[null]	[null]
2	2	2	1	✅	[null]	[null]	[null]	[null]	[null]	[null]	Iris-setosa	1.0
3	2	3	1	❌	❌	petalwidthcm	1.750000	0.165714964773788	6	7	[null]	[null]
4	2	6	2	✅	[null]	[null]	[null]	[null]	[null]	[null]	Iris-versicolor	0.885714285714286
5	2	7	2	✅	[null]	[null]	[null]	[null]	[null]	[null]	Iris-virginica	0.96969696969697

Out[51]: