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verticapy.machine_learning.vertica.naive_bayes.NaiveBayes.confusion_matrix

NaiveBayes.confusion_matrix(pos_label: Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] | None = None, cutoff: Annotated[int | float | Decimal, 'Python Numbers'] | None = None) → TableSample

Computes the model confusion matrix.

Parameters

pos_label: PythonScalar, optional

Label to consider as positive. All the other classes are merged and considered as negative for multiclass classification. If the ‘pos_label’ is not defined, the entire confusion matrix is drawn.

cutoff: PythonNumber, optional

Cutoff for which the tested category is accepted as a prediction. It is only used if ‘pos_label’ is defined.

Returns

TableSample

confusion matrix.

Examples

For this example, we will use the Iris dataset.

import verticapy.datasets as vpd

data = vpd.load_iris()

train, test = data.train_test_split(test_size = 0.2)

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)
1	3.3	4.5	5.6	7.8	Iris-setosa
2	3.3	4.5	5.6	7.8	Iris-setosa
3	3.3	4.5	5.6	7.8	Iris-setosa
4	3.3	4.5	5.6	7.8	Iris-setosa
5	3.3	4.5	5.6	7.8	Iris-setosa
6	3.3	4.5	5.6	7.8	Iris-setosa
7	3.3	4.5	5.6	7.8	Iris-setosa
8	3.3	4.5	5.6	7.8	Iris-setosa
9	3.3	4.5	5.6	7.8	Iris-setosa
10	3.3	4.5	5.6	7.8	Iris-setosa
11	3.3	4.5	5.6	7.8	Iris-setosa
12	3.3	4.5	5.6	7.8	Iris-setosa
13	3.3	4.5	5.6	7.8	Iris-setosa
14	3.3	4.5	5.6	7.8	Iris-setosa
15	3.3	4.5	5.6	7.8	Iris-setosa
16	3.3	4.5	5.6	7.8	Iris-setosa
17	3.3	4.5	5.6	7.8	Iris-setosa
18	3.3	4.5	5.6	7.8	Iris-setosa
19	3.3	4.5	5.6	7.8	Iris-setosa
20	3.3	4.5	5.6	7.8	Iris-setosa
21	3.3	4.5	5.6	7.8	Iris-setosa
22	3.3	4.5	5.6	7.8	Iris-setosa
23	3.3	4.5	5.6	7.8	Iris-setosa
24	3.3	4.5	5.6	7.8	Iris-setosa
25	3.3	4.5	5.6	7.8	Iris-setosa
26	3.3	4.5	5.6	7.8	Iris-setosa
27	4.3	3.0	1.1	0.1	Iris-setosa
28	4.3	4.7	9.6	1.8	Iris-virginica
29	4.3	4.7	9.6	1.8	Iris-virginica
30	4.3	4.7	9.6	1.8	Iris-virginica
31	4.3	4.7	9.6	1.8	Iris-virginica
32	4.3	4.7	9.6	1.8	Iris-virginica
33	4.3	4.7	9.6	1.8	Iris-virginica
34	4.3	4.7	9.6	1.8	Iris-virginica
35	4.3	4.7	9.6	1.8	Iris-virginica
36	4.3	4.7	9.6	1.8	Iris-virginica
37	4.3	4.7	9.6	1.8	Iris-virginica
38	4.3	4.7	9.6	1.8	Iris-virginica
39	4.3	4.7	9.6	1.8	Iris-virginica
40	4.3	4.7	9.6	1.8	Iris-virginica
41	4.3	4.7	9.6	1.8	Iris-virginica
42	4.3	4.7	9.6	1.8	Iris-virginica
43	4.3	4.7	9.6	1.8	Iris-virginica
44	4.3	4.7	9.6	1.8	Iris-virginica
45	4.3	4.7	9.6	1.8	Iris-virginica
46	4.3	4.7	9.6	1.8	Iris-virginica
47	4.3	4.7	9.6	1.8	Iris-virginica
48	4.3	4.7	9.6	1.8	Iris-virginica
49	4.3	4.7	9.6	1.8	Iris-virginica
50	4.3	4.7	9.6	1.8	Iris-virginica
51	4.3	4.7	9.6	1.8	Iris-virginica
52	4.3	4.7	9.6	1.8	Iris-virginica
53	4.3	4.7	9.6	1.8	Iris-virginica
54	4.4	2.9	1.4	0.2	Iris-setosa
55	4.4	3.0	1.3	0.2	Iris-setosa
56	4.4	3.2	1.3	0.2	Iris-setosa
57	4.5	2.3	1.3	0.3	Iris-setosa
58	4.6	3.1	1.5	0.2	Iris-setosa
59	4.6	3.2	1.4	0.2	Iris-setosa
60	4.6	3.4	1.4	0.3	Iris-setosa
61	4.6	3.6	1.0	0.2	Iris-setosa
62	4.7	3.2	1.3	0.2	Iris-setosa
63	4.7	3.2	1.6	0.2	Iris-setosa
64	4.8	3.0	1.4	0.1	Iris-setosa
65	4.8	3.0	1.4	0.3	Iris-setosa
66	4.8	3.1	1.6	0.2	Iris-setosa
67	4.8	3.4	1.6	0.2	Iris-setosa
68	4.8	3.4	1.9	0.2	Iris-setosa
69	4.9	2.4	3.3	1.0	Iris-versicolor
70	4.9	2.5	4.5	1.7	Iris-virginica
71	4.9	3.0	1.4	0.2	Iris-setosa
72	4.9	3.1	1.5	0.1	Iris-setosa
73	4.9	3.1	1.5	0.1	Iris-setosa
74	4.9	3.1	1.5	0.1	Iris-setosa
75	5.0	2.0	3.5	1.0	Iris-versicolor
76	5.0	2.3	3.3	1.0	Iris-versicolor
77	5.0	3.0	1.6	0.2	Iris-setosa
78	5.0	3.2	1.2	0.2	Iris-setosa
79	5.0	3.3	1.4	0.2	Iris-setosa
80	5.0	3.4	1.5	0.2	Iris-setosa
81	5.0	3.4	1.6	0.4	Iris-setosa
82	5.0	3.5	1.3	0.3	Iris-setosa
83	5.0	3.5	1.6	0.6	Iris-setosa
84	5.0	3.6	1.4	0.2	Iris-setosa
85	5.1	2.5	3.0	1.1	Iris-versicolor
86	5.1	3.3	1.7	0.5	Iris-setosa
87	5.1	3.4	1.5	0.2	Iris-setosa
88	5.1	3.5	1.4	0.2	Iris-setosa
89	5.1	3.5	1.4	0.3	Iris-setosa
90	5.1	3.7	1.5	0.4	Iris-setosa
91	5.1	3.8	1.5	0.3	Iris-setosa
92	5.1	3.8	1.6	0.2	Iris-setosa
93	5.1	3.8	1.9	0.4	Iris-setosa
94	5.2	2.7	3.9	1.4	Iris-versicolor
95	5.2	3.4	1.4	0.2	Iris-setosa
96	5.2	3.5	1.5	0.2	Iris-setosa
97	5.2	4.1	1.5	0.1	Iris-setosa
98	5.3	3.7	1.5	0.2	Iris-setosa
99	5.4	3.0	4.5	1.5	Iris-versicolor
100	5.4	3.4	1.5	0.4	Iris-setosa

Rows: 1-100 | Columns: 5

Let’s import the model:

from verticapy.machine_learning.vertica import NearestCentroid

Then we can create the model:

model = NearestCentroid(p = 2)

We can now fit the model:

model.fit(
    train,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "Species",
    test,
)

We can get the confusion matrix:

model.confusion_matrix()
Out[24]: 
array([[ 3,  8,  0],
       [ 0, 12,  0],
       [ 0,  4, 17]])

To get the confusion matrix of a particular class:

model.confusion_matrix(pos_label= "Iris-setosa")
Out[25]: 
array([[33,  0],
       [ 0, 11]])

Important

For this example, a specific model is utilized, and it may not correspond exactly to the model you are working with. To see a comprehensive example specific to your class of interest, please refer to that particular class.