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verticapy.machine_learning.vertica.cluster.NearestCentroid.predict_proba

NearestCentroid.predict_proba(vdf: str | vDataFrame, X: str | list[str] | None = None, name: str | None = None, pos_label: bool | float | str | timedelta | datetime | None = None, inplace: bool = True) → vDataFrame

Returns the model’s probabilities using the input relation.

Parameters

vdf: SQLRelation

Object used to run the prediction. You can also specify a customized relation, but you must enclose it with an alias. For example, (SELECT 1) x is valid, whereas (SELECT 1) and “SELECT 1” are invalid.

X: SQLColumns, optional

List of the columns used to deploy the models. If empty, the model predictors are used.

name: str, optional

Name of the added vDataColumn. If empty, a name is generated.

pos_label: PythonScalar, optional

Class label. For binary classification, this can be either 1 or 0.

inplace: bool, optional

If set to True, the prediction is added to the vDataFrame.

Returns

vDataFrame

the input object.

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	3.3	4.5	5.6	7.8	Iris-setosa
28	3.3	4.5	5.6	7.8	Iris-setosa
29	3.3	4.5	5.6	7.8	Iris-setosa
30	3.3	4.5	5.6	7.8	Iris-setosa
31	3.3	4.5	5.6	7.8	Iris-setosa
32	3.3	4.5	5.6	7.8	Iris-setosa
33	3.3	4.5	5.6	7.8	Iris-setosa
34	3.3	4.5	5.6	7.8	Iris-setosa
35	3.3	4.5	5.6	7.8	Iris-setosa
36	3.3	4.5	5.6	7.8	Iris-setosa
37	3.3	4.5	5.6	7.8	Iris-setosa
38	3.3	4.5	5.6	7.8	Iris-setosa
39	3.3	4.5	5.6	7.8	Iris-setosa
40	3.3	4.5	5.6	7.8	Iris-setosa
41	3.3	4.5	5.6	7.8	Iris-setosa
42	3.3	4.5	5.6	7.8	Iris-setosa
43	4.3	3.0	1.1	0.1	Iris-setosa
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	4.3	4.7	9.6	1.8	Iris-virginica
55	4.3	4.7	9.6	1.8	Iris-virginica
56	4.3	4.7	9.6	1.8	Iris-virginica
57	4.3	4.7	9.6	1.8	Iris-virginica
58	4.3	4.7	9.6	1.8	Iris-virginica
59	4.3	4.7	9.6	1.8	Iris-virginica
60	4.3	4.7	9.6	1.8	Iris-virginica
61	4.3	4.7	9.6	1.8	Iris-virginica
62	4.3	4.7	9.6	1.8	Iris-virginica
63	4.3	4.7	9.6	1.8	Iris-virginica
64	4.3	4.7	9.6	1.8	Iris-virginica
65	4.3	4.7	9.6	1.8	Iris-virginica
66	4.3	4.7	9.6	1.8	Iris-virginica
67	4.3	4.7	9.6	1.8	Iris-virginica
68	4.3	4.7	9.6	1.8	Iris-virginica
69	4.3	4.7	9.6	1.8	Iris-virginica
70	4.3	4.7	9.6	1.8	Iris-virginica
71	4.3	4.7	9.6	1.8	Iris-virginica
72	4.3	4.7	9.6	1.8	Iris-virginica
73	4.3	4.7	9.6	1.8	Iris-virginica
74	4.3	4.7	9.6	1.8	Iris-virginica
75	4.3	4.7	9.6	1.8	Iris-virginica
76	4.3	4.7	9.6	1.8	Iris-virginica
77	4.3	4.7	9.6	1.8	Iris-virginica
78	4.3	4.7	9.6	1.8	Iris-virginica
79	4.3	4.7	9.6	1.8	Iris-virginica
80	4.3	4.7	9.6	1.8	Iris-virginica
81	4.3	4.7	9.6	1.8	Iris-virginica
82	4.3	4.7	9.6	1.8	Iris-virginica
83	4.3	4.7	9.6	1.8	Iris-virginica
84	4.3	4.7	9.6	1.8	Iris-virginica
85	4.3	4.7	9.6	1.8	Iris-virginica
86	4.4	2.9	1.4	0.2	Iris-setosa
87	4.4	3.0	1.3	0.2	Iris-setosa
88	4.4	3.2	1.3	0.2	Iris-setosa
89	4.5	2.3	1.3	0.3	Iris-setosa
90	4.6	3.1	1.5	0.2	Iris-setosa
91	4.6	3.2	1.4	0.2	Iris-setosa
92	4.6	3.4	1.4	0.3	Iris-setosa
93	4.6	3.6	1.0	0.2	Iris-setosa
94	4.7	3.2	1.3	0.2	Iris-setosa
95	4.7	3.2	1.6	0.2	Iris-setosa
96	4.8	3.0	1.4	0.1	Iris-setosa
97	4.8	3.0	1.4	0.3	Iris-setosa
98	4.8	3.1	1.6	0.2	Iris-setosa
99	4.8	3.4	1.6	0.2	Iris-setosa
100	4.8	3.4	1.9	0.2	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 then get the prediction:

model.predict_proba(test, name = "prediction"

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)	123 prediction_irissetosa Float(22)	123 prediction_irisversicolor Float(22)	123 prediction_irisvirginica Float(22)
1	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
2	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
3	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
4	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
5	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
6	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
7	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
8	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
9	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
10	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
11	4.3	3.0	1.1	0.1	Iris-setosa	0.355778626178296	0.411415408789783	0.232805965031921
12	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
13	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
14	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
15	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
16	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
17	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
18	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
19	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
20	4.5	2.3	1.3	0.3	Iris-setosa	0.344952271231825	0.426833061598544	0.228214667169631
21	4.6	3.6	1.0	0.2	Iris-setosa	0.361302047815298	0.406831958698383	0.231865993486319
22	5.0	2.3	3.3	1.0	Iris-versicolor	0.256805937479663	0.556374153126265	0.186819909394073
23	5.0	3.0	1.6	0.2	Iris-setosa	0.330746158275763	0.445672527044444	0.223581314679793
24	5.1	2.5	3.0	1.1	Iris-versicolor	0.27546245291216	0.539683192053903	0.184854355033937
25	5.1	3.5	1.4	0.2	Iris-setosa	0.340913120090556	0.43213952000478	0.226947359904664
26	5.1	3.8	1.6	0.2	Iris-setosa	0.3403757375287	0.431883779179672	0.227740483291628
27	5.1	3.8	1.9	0.4	Iris-setosa	0.337877918666458	0.440271239094929	0.221850842238613
28	5.2	2.7	3.9	1.4	Iris-versicolor	0.200991801357919	0.645493165853817	0.153515032788264
29	5.2	3.5	1.5	0.2	Iris-setosa	0.335866875638006	0.438219986008347	0.225913138353647
30	5.6	2.5	3.9	1.1	Iris-versicolor	0.145146280655112	0.729179407531857	0.125674311813031
31	5.6	2.7	4.2	1.3	Iris-versicolor	0.104648075860941	0.800260741236821	0.0950911829022381
32	5.6	2.9	3.6	1.3	Iris-versicolor	0.184502245356086	0.677017995803862	0.138479758840052
33	5.7	2.9	4.2	1.3	Iris-versicolor	0.0893021830435014	0.828995491188573	0.0817023257679252
34	5.8	2.8	5.1	2.4	Iris-virginica	0.233756509075543	0.507826407654442	0.258417083270014
35	5.8	4.0	1.2	0.2	Iris-setosa	0.337820434753938	0.429451879343579	0.232727685902483
36	5.9	3.2	4.8	1.8	Iris-versicolor	0.171008816761464	0.642184589298334	0.186806593940202
37	6.3	2.8	5.1	1.5	Iris-virginica	0.150624840198324	0.645958333915994	0.203416825885682
38	6.3	3.3	6.0	2.5	Iris-virginica	0.215738004352272	0.364343241382716	0.419918754265012
39	6.4	2.7	5.3	1.9	Iris-virginica	0.184543840320151	0.55644805204272	0.25900810763713
40	6.5	3.0	5.5	1.8	Iris-virginica	0.187851392923549	0.511381040666508	0.300767566409943
41	6.6	2.9	4.6	1.3	Iris-versicolor	0.129829271938938	0.720218911193788	0.149951816867274
42	6.7	2.5	5.8	1.8	Iris-virginica	0.193715298538593	0.472098041603156	0.334186659858251
43	6.7	3.0	5.0	1.7	Iris-versicolor	0.173842726264971	0.601493315238816	0.224663958496213
44	6.8	3.2	5.9	2.3	Iris-virginica	0.211859518917542	0.403892578831004	0.384247902251454
45	7.2	3.6	6.1	2.5	Iris-virginica	0.22316702386903	0.364710746974964	0.412122229156007
46	7.9	3.8	6.4	2.0	Iris-virginica	0.219359328115032	0.361146089781331	0.419494582103637
47	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
48	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721
49	3.3	4.5	5.6	7.8	Iris-setosa	0.424609645843956	0.272343574903873	0.30304677925217
50	4.3	4.7	9.6	1.8	Iris-virginica	0.215532149107533	0.239395773632746	0.545072077259721

Rows: 1-50 | Columns: 8

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.