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verticapy.machine_learning.vertica.cluster.NearestCentroid.predict_proba

NearestCentroid.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	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 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	0.384288101767972	0.291572597552576	0.324139300679451
2	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
3	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
4	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
5	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
6	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
7	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
8	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
9	4.3	4.7	9.6	1.8	Iris-virginica	0.221260825414011	0.25453816392075	0.524201010665239
10	4.3	4.7	9.6	1.8	Iris-virginica	0.221260825414011	0.25453816392075	0.524201010665239
11	4.3	4.7	9.6	1.8	Iris-virginica	0.221260825414011	0.25453816392075	0.524201010665239
12	4.4	3.0	1.3	0.2	Iris-setosa	0.40068689528239	0.383847020922304	0.215466083795306
13	4.6	3.4	1.4	0.3	Iris-setosa	0.405614959173674	0.382446802495275	0.211938238331051
14	4.9	3.1	1.5	0.1	Iris-setosa	0.38208852581726	0.403317823834009	0.214593650348731
15	4.9	3.1	1.5	0.1	Iris-setosa	0.38208852581726	0.403317823834009	0.214593650348731
16	5.0	3.2	1.2	0.2	Iris-setosa	0.390847228656931	0.394222147314579	0.21493062402849
17	5.0	3.3	1.4	0.2	Iris-setosa	0.38868946511252	0.397939691056949	0.213370843830531
18	5.1	2.5	3.0	1.1	Iris-versicolor	0.324560985338619	0.499335784439514	0.176103230221867
19	5.1	3.8	1.5	0.3	Iris-setosa	0.395837353099702	0.391261855738943	0.212900791161355
20	5.1	3.8	1.6	0.2	Iris-setosa	0.389404810866544	0.396051677686342	0.214543511447113
21	5.1	3.8	1.9	0.4	Iris-setosa	0.390967239203815	0.401167266360277	0.207865494435908
22	5.2	4.1	1.5	0.1	Iris-setosa	0.388543475386626	0.391173927473474	0.2202825971399
23	5.4	3.7	1.5	0.2	Iris-setosa	0.381083762101294	0.40369506488497	0.215221173013736
24	5.5	2.4	3.7	1.0	Iris-versicolor	0.20724309361995	0.643126135641692	0.149630770738358
25	5.6	2.9	3.6	1.3	Iris-versicolor	0.215162674443197	0.648661324371718	0.136176001185085
26	5.7	4.4	1.5	0.4	Iris-setosa	0.388901685768441	0.390985842366843	0.220112471864716
27	5.8	2.7	3.9	1.2	Iris-versicolor	0.129548994406608	0.771415224875671	0.0990357807177208
28	5.8	2.7	5.1	1.9	Iris-virginica	0.188891148778598	0.574398822487549	0.236710028733853
29	5.8	4.0	1.2	0.2	Iris-setosa	0.380316918790637	0.3976454573906	0.222037623818763
30	6.1	2.8	4.7	1.2	Iris-versicolor	0.106787314763239	0.772970026996526	0.120242658240236
31	6.1	3.0	4.9	1.8	Iris-virginica	0.169213744437144	0.631021851538608	0.199764404024248
32	6.3	2.5	5.0	1.9	Iris-virginica	0.180723231973632	0.595942543213244	0.223334224813124
33	6.4	2.8	5.6	2.1	Iris-virginica	0.198983966915376	0.454916248457838	0.346099784626786
34	6.4	2.8	5.6	2.2	Iris-virginica	0.203395494289261	0.447157371595391	0.349447134115347
35	6.7	3.1	4.7	1.5	Iris-versicolor	0.171611586419233	0.62843104371555	0.199957369865217
36	6.7	3.3	5.7	2.1	Iris-virginica	0.202164199114561	0.412077228114874	0.385758572770565
37	6.9	3.1	5.1	2.3	Iris-virginica	0.223114409141613	0.482454689834769	0.294430901023617
38	7.2	3.2	6.0	1.8	Iris-virginica	0.197289227022042	0.392026322306197	0.410684450671761
39	7.9	3.8	6.4	2.0	Iris-virginica	0.214163871236351	0.346216832084779	0.43961929667887
40	4.3	4.7	9.6	1.8	Iris-virginica	0.221260825414011	0.25453816392075	0.524201010665239
41	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
42	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
43	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451
44	3.3	4.5	5.6	7.8	Iris-setosa	0.384288101767972	0.291572597552576	0.324139300679451

Rows: 1-44 | 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.