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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	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	0.38864692275889	0.288923285635048	0.322429791606062
2	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
3	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
4	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
5	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
6	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
7	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
8	3.3	4.5	5.6	7.8	Iris-setosa	0.38864692275889	0.288923285635048	0.322429791606062
9	4.3	4.7	9.6	1.8	Iris-virginica	0.223728569670304	0.253374401502291	0.522897028827405
10	4.3	4.7	9.6	1.8	Iris-virginica	0.223728569670304	0.253374401502291	0.522897028827405
11	4.3	4.7	9.6	1.8	Iris-virginica	0.223728569670304	0.253374401502291	0.522897028827405
12	4.3	4.7	9.6	1.8	Iris-virginica	0.223728569670304	0.253374401502291	0.522897028827405
13	4.3	4.7	9.6	1.8	Iris-virginica	0.223728569670304	0.253374401502291	0.522897028827405
14	4.3	4.7	9.6	1.8	Iris-virginica	0.223728569670304	0.253374401502291	0.522897028827405
15	4.6	3.4	1.4	0.3	Iris-setosa	0.395716739562585	0.390930215747962	0.213353044689453
16	4.7	3.2	1.6	0.2	Iris-setosa	0.380978471398448	0.405662345287103	0.21335918331445
17	4.9	2.4	3.3	1.0	Iris-versicolor	0.293402885511769	0.525853087440262	0.180744027047969
18	4.9	3.1	1.5	0.1	Iris-setosa	0.372150203589877	0.412279208908002	0.21557058750212
19	5.0	3.0	1.6	0.2	Iris-setosa	0.368252054290535	0.419474672878694	0.212273272830771
20	5.0	3.2	1.2	0.2	Iris-setosa	0.381373654652462	0.402650453840864	0.215975891506674
21	5.0	3.4	1.6	0.4	Iris-setosa	0.384326468343881	0.407179543960736	0.208493987695383
22	5.1	3.8	1.5	0.3	Iris-setosa	0.386697550312038	0.399379857722991	0.213922591964971
23	5.2	2.7	3.9	1.4	Iris-versicolor	0.217308372757308	0.63519566558057	0.147495961662122
24	5.4	3.0	4.5	1.5	Iris-versicolor	0.176036978195889	0.665646961452019	0.158316060352092
25	5.4	3.4	1.7	0.2	Iris-setosa	0.360695080305827	0.42695450085917	0.212350418835003
26	5.6	2.5	3.9	1.1	Iris-versicolor	0.148664298278447	0.733857733657943	0.117477968063611
27	5.6	2.8	4.9	2.0	Iris-virginica	0.210915117805586	0.559721160142897	0.229363722051517
28	5.8	2.7	5.1	1.9	Iris-virginica	0.194973882973042	0.554789594005002	0.250236523021956
29	5.9	3.0	4.2	1.5	Iris-versicolor	0.0967305890937725	0.82149904312595	0.0817703677802771
30	5.9	3.2	4.8	1.8	Iris-versicolor	0.189505265756689	0.600954016406516	0.209540717836794
31	6.0	2.7	5.1	1.6	Iris-versicolor	0.169661643583201	0.600377091681597	0.229961264735202
32	6.2	3.4	5.4	2.3	Iris-virginica	0.225133165890698	0.420162694920728	0.354704139188574
33	6.3	2.3	4.4	1.3	Iris-versicolor	0.13179578587771	0.732959116204922	0.135245097917368
34	6.3	3.4	5.6	2.4	Iris-virginica	0.220718310983993	0.386504241597897	0.39277744741811
35	6.4	2.8	5.6	2.2	Iris-virginica	0.206512392361641	0.43323478130485	0.360252826333509
36	6.7	3.3	5.7	2.1	Iris-virginica	0.204938251127183	0.400208443992383	0.394853304880435
37	6.7	3.3	5.7	2.5	Iris-virginica	0.220644614762744	0.380949795680825	0.398405589556431
38	6.8	3.2	5.9	2.3	Iris-virginica	0.205849293884283	0.370825915879584	0.423324790236133
39	6.9	3.1	4.9	1.5	Iris-versicolor	0.190293464479833	0.560268569561951	0.249437965958216
40	6.9	3.2	5.7	2.3	Iris-virginica	0.213981479128166	0.39702802666814	0.388990494203694
41	7.0	3.2	4.7	1.4	Iris-versicolor	0.194552113300219	0.568679056440189	0.236768830259592
42	7.2	3.2	6.0	1.8	Iris-virginica	0.199146393120126	0.383033974441057	0.417819632438817
43	7.6	3.0	6.6	2.1	Iris-virginica	0.203203831897157	0.337630569450462	0.459165598652381
44	7.9	3.8	6.4	2.0	Iris-virginica	0.216390596367537	0.341348109359985	0.442261294272478

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.