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verticapy.machine_learning.vertica.neighbors.KNeighborsClassifier.predict_proba

KNeighborsClassifier.predict_proba(vdf: Annotated[str | vDataFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str | None = None, pos_label: Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] | 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	4.6	3.6	1.0	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.7	3.2	1.6	0.2	Iris-setosa
4	4.8	3.0	1.4	0.1	Iris-setosa
5	4.8	3.1	1.6	0.2	Iris-setosa
6	4.8	3.4	1.9	0.2	Iris-setosa
7	4.9	3.0	1.4	0.2	Iris-setosa
8	4.9	3.1	1.5	0.1	Iris-setosa
9	4.9	3.1	1.5	0.1	Iris-setosa
10	4.9	3.1	1.5	0.1	Iris-setosa
11	5.0	2.3	3.3	1.0	Iris-versicolor
12	5.0	3.4	1.5	0.2	Iris-setosa
13	5.1	3.5	1.4	0.2	Iris-setosa
14	5.4	3.0	4.5	1.5	Iris-versicolor
15	5.4	3.4	1.5	0.4	Iris-setosa
16	5.4	3.9	1.3	0.4	Iris-setosa
17	5.5	2.4	3.7	1.0	Iris-versicolor
18	5.5	2.4	3.8	1.1	Iris-versicolor
19	5.6	2.7	4.2	1.3	Iris-versicolor
20	5.7	3.0	4.2	1.2	Iris-versicolor
21	5.7	4.4	1.5	0.4	Iris-setosa
22	5.8	2.8	5.1	2.4	Iris-virginica
23	5.9	3.2	4.8	1.8	Iris-versicolor
24	6.1	3.0	4.6	1.4	Iris-versicolor
25	6.1	3.0	4.9	1.8	Iris-virginica
26	6.3	2.5	4.9	1.5	Iris-versicolor
27	6.3	3.3	4.7	1.6	Iris-versicolor
28	6.3	3.3	6.0	2.5	Iris-virginica
29	6.4	2.9	4.3	1.3	Iris-versicolor
30	6.5	3.0	5.5	1.8	Iris-virginica
31	6.5	3.0	5.8	2.2	Iris-virginica
32	6.7	3.0	5.0	1.7	Iris-versicolor
33	6.8	2.8	4.8	1.4	Iris-versicolor
34	6.8	3.2	5.9	2.3	Iris-virginica
35	7.0	3.2	4.7	1.4	Iris-versicolor
36	7.1	3.0	5.9	2.1	Iris-virginica
37	7.7	3.8	6.7	2.2	Iris-virginica
38	4.4	2.9	1.4	0.2	Iris-setosa
39	4.5	2.3	1.3	0.3	Iris-setosa
40	4.8	3.4	1.6	0.2	Iris-setosa
41	5.0	2.0	3.5	1.0	Iris-versicolor
42	5.1	3.3	1.7	0.5	Iris-setosa
43	5.1	3.4	1.5	0.2	Iris-setosa
44	5.2	2.7	3.9	1.4	Iris-versicolor
45	5.2	3.5	1.5	0.2	Iris-setosa
46	5.2	4.1	1.5	0.1	Iris-setosa
47	5.4	3.9	1.7	0.4	Iris-setosa
48	5.5	3.5	1.3	0.2	Iris-setosa
49	5.6	3.0	4.1	1.3	Iris-versicolor
50	5.8	2.7	3.9	1.2	Iris-versicolor
51	5.8	2.7	5.1	1.9	Iris-virginica
52	5.8	2.7	5.1	1.9	Iris-virginica
53	5.9	3.0	4.2	1.5	Iris-versicolor
54	5.9	3.0	5.1	1.8	Iris-virginica
55	6.0	2.7	5.1	1.6	Iris-versicolor
56	6.0	2.9	4.5	1.5	Iris-versicolor
57	6.1	2.8	4.7	1.2	Iris-versicolor
58	6.2	2.8	4.8	1.8	Iris-virginica
59	6.2	2.9	4.3	1.3	Iris-versicolor
60	6.3	2.3	4.4	1.3	Iris-versicolor
61	6.3	2.7	4.9	1.8	Iris-virginica
62	6.4	3.2	5.3	2.3	Iris-virginica
63	6.5	2.8	4.6	1.5	Iris-versicolor
64	6.5	3.0	5.2	2.0	Iris-virginica
65	6.5	3.2	5.1	2.0	Iris-virginica
66	6.6	2.9	4.6	1.3	Iris-versicolor
67	6.6	3.0	4.4	1.4	Iris-versicolor
68	6.7	3.1	4.4	1.4	Iris-versicolor
69	6.7	3.1	4.7	1.5	Iris-versicolor
70	6.9	3.1	4.9	1.5	Iris-versicolor
71	6.9	3.1	5.4	2.1	Iris-virginica
72	6.9	3.2	5.7	2.3	Iris-virginica
73	7.2	3.0	5.8	1.6	Iris-virginica
74	7.2	3.2	6.0	1.8	Iris-virginica
75	7.3	2.9	6.3	1.8	Iris-virginica
76	7.7	2.6	6.9	2.3	Iris-virginica
77	3.3	4.5	5.6	7.8	Iris-setosa
78	3.3	4.5	5.6	7.8	Iris-setosa
79	3.3	4.5	5.6	7.8	Iris-setosa
80	3.3	4.5	5.6	7.8	Iris-setosa
81	3.3	4.5	5.6	7.8	Iris-setosa
82	3.3	4.5	5.6	7.8	Iris-setosa
83	3.3	4.5	5.6	7.8	Iris-setosa
84	3.3	4.5	5.6	7.8	Iris-setosa
85	3.3	4.5	5.6	7.8	Iris-setosa
86	3.3	4.5	5.6	7.8	Iris-setosa
87	3.3	4.5	5.6	7.8	Iris-setosa
88	3.3	4.5	5.6	7.8	Iris-setosa
89	3.3	4.5	5.6	7.8	Iris-setosa
90	3.3	4.5	5.6	7.8	Iris-setosa
91	3.3	4.5	5.6	7.8	Iris-setosa
92	3.3	4.5	5.6	7.8	Iris-setosa
93	3.3	4.5	5.6	7.8	Iris-setosa
94	3.3	4.5	5.6	7.8	Iris-setosa
95	3.3	4.5	5.6	7.8	Iris-setosa
96	3.3	4.5	5.6	7.8	Iris-setosa
97	3.3	4.5	5.6	7.8	Iris-setosa
98	3.3	4.5	5.6	7.8	Iris-setosa
99	3.3	4.5	5.6	7.8	Iris-setosa
100	3.3	4.5	5.6	7.8	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	4.9	3.0	1.4	0.2	Iris-setosa	0.317967516707116	0.459057563568108	0.222974919724776
2	4.9	3.1	1.5	0.1	Iris-setosa	0.314284906651631	0.461641571329461	0.224073522018908
3	4.9	3.1	1.5	0.1	Iris-setosa	0.314284906651631	0.461641571329461	0.224073522018908
4	5.7	3.0	4.2	1.2	Iris-versicolor	0.0828847138083736	0.837946045022835	0.0791692411687911
5	5.7	4.4	1.5	0.4	Iris-setosa	0.32506287596827	0.444016073813607	0.230921050218123
6	6.1	3.0	4.9	1.8	Iris-virginica	0.169515858666329	0.63332625643537	0.197157884898301
7	6.3	3.3	4.7	1.6	Iris-versicolor	0.16462764811662	0.648865524813052	0.186506827070328
8	6.7	3.0	5.0	1.7	Iris-versicolor	0.182887407929942	0.583135435818147	0.233977156251911
9	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
10	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
11	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
12	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
13	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
14	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
15	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
16	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
17	4.9	2.5	4.5	1.7	Iris-virginica	0.221143952246397	0.586037098564854	0.192818949188749
18	5.0	3.5	1.3	0.3	Iris-setosa	0.327443096788383	0.448191086284317	0.224365816927299
19	5.1	3.5	1.4	0.3	Iris-setosa	0.322499957106755	0.454441245776466	0.223058797116779
20	5.5	2.3	4.0	1.3	Iris-versicolor	0.133748232072368	0.748524251633644	0.117727516293988
21	5.7	2.5	5.0	2.0	Iris-virginica	0.196135542160966	0.586457103334378	0.217407354504656
22	6.0	3.4	4.5	1.6	Iris-versicolor	0.16195302519588	0.673679723513363	0.164367251290757
23	6.3	2.8	5.1	1.5	Iris-virginica	0.161111878012977	0.624334647638356	0.214553474348668
24	6.7	2.5	5.8	1.8	Iris-virginica	0.200126012524417	0.465931936926012	0.333942050549571
25	6.8	3.0	5.5	2.1	Iris-virginica	0.212781805671701	0.471641950097363	0.315576244230936
26	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
27	3.3	4.5	5.6	7.8	Iris-setosa	0.44384381098507	0.263280552952575	0.292875636062355
28	5.2	4.1	1.5	0.1	Iris-setosa	0.322731674668094	0.445845943356347	0.231422381975558
29	5.8	2.7	5.1	1.9	Iris-virginica	0.187878576757847	0.586427918604331	0.225693504637823
30	5.9	3.0	4.2	1.5	Iris-versicolor	0.0835675481962282	0.839243263957041	0.0771891878467311
31	6.4	3.2	5.3	2.3	Iris-virginica	0.226140096672454	0.477669046996421	0.296190856331125
32	6.5	3.0	5.2	2.0	Iris-virginica	0.200851531653541	0.534463143313086	0.264685325033373
33	6.6	3.0	4.4	1.4	Iris-versicolor	0.142003130915815	0.705135109608472	0.152861759475713
34	7.2	3.0	5.8	1.6	Iris-virginica	0.202645493900566	0.448455260280059	0.348899245819374
35	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
36	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
37	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
38	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
39	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
40	4.8	3.0	1.4	0.3	Iris-setosa	0.322576417324968	0.455576900783346	0.221846681891686
41	5.0	3.3	1.4	0.2	Iris-setosa	0.319569266052586	0.456633605202832	0.223797128744582
42	5.1	3.8	1.5	0.3	Iris-setosa	0.324610237729026	0.450583137444219	0.224806624826755
43	5.3	3.7	1.5	0.2	Iris-setosa	0.316133083300556	0.458789867616294	0.22507704908315
44	5.7	2.8	4.5	1.3	Iris-versicolor	0.0848750459451505	0.826979693873227	0.0881452601816223
45	6.1	2.9	4.7	1.4	Iris-versicolor	0.115753808885087	0.751870266889735	0.132375924225178
46	6.2	2.2	4.5	1.5	Iris-versicolor	0.133987145978133	0.724753507633539	0.141259346388328
47	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
48	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
49	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509
50	4.3	4.7	9.6	1.8	Iris-virginica	0.210211777777219	0.228461472497272	0.561326749725509

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.