Model.features_importance¶

In [ ]:

Model.features_importance(ax=None,
                          tree_id: int = None, 
                          show: bool = True, 
                          **style_kwds,)

Computes the importance of the features of the model.

Parameters¶

Name	Type	Optional	Description
ax	Matplotlib axes object	✓	The axes to plot on.
tree_id	int	✓	Tree ID in case of Tree Based models.
show	bool	✓	If set to True, draw the features importance.
**style_kwds	any	✓	Any optional parameter to pass to the Matplotlib functions.

Returns¶

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

Example¶

In [4]:

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.drop()
model.fit("public.iris", 
          ["PetalLengthCm", "PetalWidthCm", "SepalWidthCm", "SepalLengthCm"], 
          "Species")
model.features_importance()

Out[4]:

	importance	sign
petalwidthcm	52.1	1
petallengthcm	40.99	1
sepallengthcm	6.91	1
sepalwidthcm	0.0	0

Rows: 1-4 | Columns: 3