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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	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	4.6	3.6	1.0	0.2	Iris-setosa	0.352949145407407	0.416114221686685	0.230936632905907
2	4.9	3.1	1.5	0.1	Iris-setosa	0.326634530807201	0.448115298832789	0.22525017036001
3	5.0	2.3	3.3	1.0	Iris-versicolor	0.247320415932064	0.571023322669057	0.181656261398879
4	5.5	2.4	3.8	1.1	Iris-versicolor	0.161640784004622	0.703329627372638	0.13502958862274
5	5.7	3.0	4.2	1.2	Iris-versicolor	0.0983529703339648	0.811041507161486	0.0906055225045491
6	5.9	3.2	4.8	1.8	Iris-versicolor	0.171466228474765	0.647574885633801	0.180958885891434
7	6.1	3.0	4.9	1.8	Iris-virginica	0.161125095281448	0.657577173781135	0.181297730937417
8	7.1	3.0	5.9	2.1	Iris-virginica	0.213177789958659	0.434153666876095	0.352668543165245
9	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
10	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
11	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
12	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
13	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
14	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
15	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
16	4.9	2.4	3.3	1.0	Iris-versicolor	0.254733460367526	0.560988864745421	0.184277674887052
17	5.0	3.5	1.6	0.6	Iris-setosa	0.341090640102741	0.442064076576967	0.216845283320293
18	5.6	2.9	3.6	1.3	Iris-versicolor	0.174587703158251	0.693656701507174	0.131755595334575
19	5.7	2.9	4.2	1.3	Iris-versicolor	0.0842466864218451	0.8394753758308	0.0762779377473549
20	5.8	4.0	1.2	0.2	Iris-setosa	0.330225737374002	0.438742117481696	0.231032145144303
21	6.1	2.8	4.0	1.3	Iris-versicolor	0.0766542631709745	0.854822168054971	0.0685235687740544
22	6.7	3.0	5.2	2.3	Iris-virginica	0.218995625147297	0.513503615350546	0.267500759502157
23	6.7	3.3	5.7	2.5	Iris-virginica	0.232308557391199	0.422100546987719	0.345590895621082
24	7.6	3.0	6.6	2.1	Iris-virginica	0.214332406029901	0.370328589291212	0.415339004678887
25	3.3	4.5	5.6	7.8	Iris-setosa	0.43447978050897	0.267960331429429	0.2975598880616
26	5.2	4.1	1.5	0.1	Iris-setosa	0.334148169080451	0.434232691704404	0.231619139215145
27	5.9	3.0	5.1	1.8	Iris-virginica	0.177854998927041	0.606521288290601	0.215623712782358
28	6.0	2.7	5.1	1.6	Iris-versicolor	0.155401792277652	0.650714384828797	0.193883822893551
29	6.0	2.9	4.5	1.5	Iris-versicolor	0.0774965999027667	0.8449593606766	0.0775440394206338
30	6.1	2.8	4.7	1.2	Iris-versicolor	0.0961383373541304	0.79462768549644	0.109233977149429
31	6.2	2.9	4.3	1.3	Iris-versicolor	0.0642654329016661	0.872342708218269	0.0633918588800652
32	6.5	3.0	5.2	2.0	Iris-virginica	0.195461600377197	0.555500261225343	0.249038138397461
33	6.6	2.9	4.6	1.3	Iris-versicolor	0.131196893358041	0.72172023688421	0.147082869757748
34	6.9	3.1	5.4	2.1	Iris-virginica	0.212234331421021	0.495289619319425	0.292476049259553
35	6.9	3.2	5.7	2.3	Iris-virginica	0.222088218508807	0.439767327250022	0.338144454241172
36	7.2	3.0	5.8	1.6	Iris-virginica	0.201165598567688	0.465214071612807	0.333620329819505
37	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
38	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
39	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
40	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
41	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
42	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
43	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
44	5.4	3.4	1.7	0.2	Iris-setosa	0.314845460556967	0.46415735159779	0.220997187845243
45	5.7	2.8	4.1	1.3	Iris-versicolor	0.086684372115061	0.837073166082932	0.0762424618020066
46	6.4	3.1	5.5	1.8	Iris-virginica	0.192499391725745	0.516471770225284	0.291028838048971
47	6.4	3.2	4.5	1.5	Iris-versicolor	0.136485270561556	0.721635959681524	0.14187876975692
48	7.7	3.0	6.1	2.3	Iris-virginica	0.227419530910214	0.404328864193179	0.368251604896607
49	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781
50	4.3	4.7	9.6	1.8	Iris-virginica	0.206665616224895	0.227562729512324	0.565771654262781

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.