Model.confusion_matrix¶

In [ ]:

Model.confusion_matrix(pos_label = None, 
                       cutoff: float = -1)

Computes the model confusion matrix.

Parameters¶

Name	Type	Optional	Description
pos_label	int / float / str	✓	Label to consider as positive. All the other classes will be merged and considered as negative in case of multi classification.
cutoff	float	✓	Cutoff for which the tested category will be accepted as prediction. In case of multiclass classification, if the cutoff is not between 0 and 1, the entire confusion matrix will be drawn.

Returns¶

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

Example¶

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# Multiclass Classification
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")
# Global Confusion Matrix
model.confusion_matrix()

	Iris-setosa	Iris-versicolor	Iris-virginica
Iris-setosa	50	0	0
Iris-versicolor	0	49	1
Iris-virginica	0	5	45

Out[45]:

In [46]:

# Confusion matrix with Iris-versicolor as 
# the positive class
model.confusion_matrix(pos_label = "Iris-versicolor",
                       cutoff = 0.33)

	Non-Iris-versicolor	Iris-versicolor
Non-Iris-versicolor	93	7
Iris-versicolor	0	50

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