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verticapy.machine_learning.vertica.ensemble.XGBClassifier.predict_proba

XGBClassifier.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	0.341178190253481	0.423602084871938	0.235219724874581
2	4.9	3.1	1.5	0.1	Iris-setosa	0.315136768278866	0.45550059402048	0.229362637700653
3	5.4	3.0	4.5	1.5	Iris-versicolor	0.146814611069092	0.708245493411139	0.144939895519769
4	5.9	3.2	4.8	1.8	Iris-versicolor	0.164274117804954	0.652992132503508	0.182733749691538
5	7.0	3.2	4.7	1.4	Iris-versicolor	0.175405587255395	0.610089507533882	0.214504905210724
6	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
7	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
8	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
9	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
10	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
11	4.6	3.4	1.4	0.3	Iris-setosa	0.333139094979605	0.437008105776147	0.229852799244248
12	5.0	3.5	1.3	0.3	Iris-setosa	0.327746945281144	0.443178853169308	0.229074201549548
13	5.5	2.6	4.4	1.2	Iris-versicolor	0.114514944360222	0.768119382924405	0.117365672715373
14	5.6	2.9	3.6	1.3	Iris-versicolor	0.163069306836036	0.705618215504533	0.131312477659431
15	5.7	2.6	3.5	1.0	Iris-versicolor	0.166838177838181	0.690703680339107	0.142458141822713
16	6.3	2.5	5.0	1.9	Iris-virginica	0.171747675624681	0.6202465005172	0.208005823858118
17	6.7	3.1	5.6	2.4	Iris-virginica	0.222587369523713	0.446465044963144	0.330947585513144
18	6.8	3.0	5.5	2.1	Iris-virginica	0.20670277950363	0.485571681863047	0.307725538633324
19	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
20	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
21	3.3	4.5	5.6	7.8	Iris-setosa	0.445641104855465	0.262919135365689	0.291439759778846
22	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
23	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
24	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
25	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
26	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
27	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
28	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
29	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
30	4.3	4.7	9.6	1.8	Iris-virginica	0.210956795357714	0.231168638953005	0.557874565689281
31	4.4	3.2	1.3	0.2	Iris-setosa	0.334818891946306	0.432357505826236	0.232823602227458
32	5.1	3.8	1.5	0.3	Iris-setosa	0.324760288507432	0.44590165766572	0.229338053826848
33	5.1	3.8	1.9	0.4	Iris-setosa	0.315547375511014	0.460078328657404	0.224374295831582
34	5.6	2.5	3.9	1.1	Iris-versicolor	0.131119828950085	0.749083125494623	0.119797045555293
35	5.7	2.8	4.1	1.3	Iris-versicolor	0.0776953508693867	0.849813317656487	0.0724913314741259
36	5.7	2.8	4.5	1.3	Iris-versicolor	0.0818829965053586	0.831146476695271	0.0869705267993705
37	6.0	2.2	4.0	1.0	Iris-versicolor	0.133376842822541	0.734614129979473	0.132009027197986
38	6.2	2.2	4.5	1.5	Iris-versicolor	0.131351002733325	0.727837485397915	0.14081151186876
39	6.4	2.8	5.6	2.1	Iris-virginica	0.202546008703037	0.484472077522006	0.312981913774957
40	7.2	3.6	6.1	2.5	Iris-virginica	0.227184363682248	0.370561324562455	0.402254311755297
41	7.7	2.8	6.7	2.0	Iris-virginica	0.212103092590052	0.367501128499361	0.420395778910587
42	4.4	2.9	1.4	0.2	Iris-setosa	0.329227125648763	0.439194206190004	0.231578668161233
43	4.5	2.3	1.3	0.3	Iris-setosa	0.325930076767447	0.443312186749893	0.23075773648266
44	5.0	2.0	3.5	1.0	Iris-versicolor	0.230411615284674	0.579744968203091	0.189843416512235
45	5.1	3.4	1.5	0.2	Iris-setosa	0.316463931200756	0.455563463134077	0.227972605665167
46	5.8	2.7	5.1	1.9	Iris-virginica	0.179958701509999	0.599534433460001	0.220506865029999
47	6.3	2.7	4.9	1.8	Iris-virginica	0.154947671673759	0.660349480455247	0.184702847870994
48	6.9	3.1	4.9	1.5	Iris-versicolor	0.173533320172181	0.603273003173245	0.223193676654574
49	6.9	3.2	5.7	2.3	Iris-virginica	0.218947041190596	0.43543924714611	0.345613711663294
50	7.3	2.9	6.3	1.8	Iris-virginica	0.202708858636143	0.399013598331545	0.398277543032311

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.