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verticapy.machine_learning.vertica.ensemble.XGBClassifier.predict_proba

XGBClassifier.predict_proba(vdf: str | vDataFrame, X: str | list[str] | None = None, name: str | None = None, pos_label: bool | float | str | timedelta | datetime | 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	3.3	4.5	5.6	7.8	Iris-setosa
28	3.3	4.5	5.6	7.8	Iris-setosa
29	3.3	4.5	5.6	7.8	Iris-setosa
30	3.3	4.5	5.6	7.8	Iris-setosa
31	3.3	4.5	5.6	7.8	Iris-setosa
32	3.3	4.5	5.6	7.8	Iris-setosa
33	3.3	4.5	5.6	7.8	Iris-setosa
34	3.3	4.5	5.6	7.8	Iris-setosa
35	3.3	4.5	5.6	7.8	Iris-setosa
36	3.3	4.5	5.6	7.8	Iris-setosa
37	3.3	4.5	5.6	7.8	Iris-setosa
38	3.3	4.5	5.6	7.8	Iris-setosa
39	3.3	4.5	5.6	7.8	Iris-setosa
40	3.3	4.5	5.6	7.8	Iris-setosa
41	3.3	4.5	5.6	7.8	Iris-setosa
42	3.3	4.5	5.6	7.8	Iris-setosa
43	4.3	3.0	1.1	0.1	Iris-setosa
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	4.3	4.7	9.6	1.8	Iris-virginica
55	4.3	4.7	9.6	1.8	Iris-virginica
56	4.3	4.7	9.6	1.8	Iris-virginica
57	4.3	4.7	9.6	1.8	Iris-virginica
58	4.3	4.7	9.6	1.8	Iris-virginica
59	4.3	4.7	9.6	1.8	Iris-virginica
60	4.3	4.7	9.6	1.8	Iris-virginica
61	4.3	4.7	9.6	1.8	Iris-virginica
62	4.3	4.7	9.6	1.8	Iris-virginica
63	4.3	4.7	9.6	1.8	Iris-virginica
64	4.3	4.7	9.6	1.8	Iris-virginica
65	4.3	4.7	9.6	1.8	Iris-virginica
66	4.3	4.7	9.6	1.8	Iris-virginica
67	4.3	4.7	9.6	1.8	Iris-virginica
68	4.3	4.7	9.6	1.8	Iris-virginica
69	4.3	4.7	9.6	1.8	Iris-virginica
70	4.3	4.7	9.6	1.8	Iris-virginica
71	4.3	4.7	9.6	1.8	Iris-virginica
72	4.3	4.7	9.6	1.8	Iris-virginica
73	4.3	4.7	9.6	1.8	Iris-virginica
74	4.3	4.7	9.6	1.8	Iris-virginica
75	4.3	4.7	9.6	1.8	Iris-virginica
76	4.3	4.7	9.6	1.8	Iris-virginica
77	4.3	4.7	9.6	1.8	Iris-virginica
78	4.3	4.7	9.6	1.8	Iris-virginica
79	4.3	4.7	9.6	1.8	Iris-virginica
80	4.3	4.7	9.6	1.8	Iris-virginica
81	4.3	4.7	9.6	1.8	Iris-virginica
82	4.3	4.7	9.6	1.8	Iris-virginica
83	4.3	4.7	9.6	1.8	Iris-virginica
84	4.3	4.7	9.6	1.8	Iris-virginica
85	4.3	4.7	9.6	1.8	Iris-virginica
86	4.4	2.9	1.4	0.2	Iris-setosa
87	4.4	3.0	1.3	0.2	Iris-setosa
88	4.4	3.2	1.3	0.2	Iris-setosa
89	4.5	2.3	1.3	0.3	Iris-setosa
90	4.6	3.1	1.5	0.2	Iris-setosa
91	4.6	3.2	1.4	0.2	Iris-setosa
92	4.6	3.4	1.4	0.3	Iris-setosa
93	4.6	3.6	1.0	0.2	Iris-setosa
94	4.7	3.2	1.3	0.2	Iris-setosa
95	4.7	3.2	1.6	0.2	Iris-setosa
96	4.8	3.0	1.4	0.1	Iris-setosa
97	4.8	3.0	1.4	0.3	Iris-setosa
98	4.8	3.1	1.6	0.2	Iris-setosa
99	4.8	3.4	1.6	0.2	Iris-setosa
100	4.8	3.4	1.9	0.2	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	0.430267758856667	0.270338784245628	0.299393456897704
2	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
3	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
4	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
5	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
6	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
7	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
8	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
9	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
10	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704
11	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
12	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
13	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
14	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
15	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
16	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
17	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
18	4.3	4.7	9.6	1.8	Iris-virginica	0.20825319532204	0.23002371232462	0.56172309235334
19	4.4	3.0	1.3	0.2	Iris-setosa	0.345109325084933	0.428217313220807	0.226673361694259
20	4.6	3.1	1.5	0.2	Iris-setosa	0.337077477481366	0.438954896777641	0.223967625740993
21	4.8	3.1	1.6	0.2	Iris-setosa	0.329347200124279	0.448863822108204	0.221788977767518
22	4.9	3.1	1.5	0.1	Iris-setosa	0.32739480463596	0.448631534932177	0.223973660431863
23	5.0	3.2	1.2	0.2	Iris-setosa	0.336624934417962	0.438571652087366	0.224803413494671
24	5.0	3.4	1.5	0.2	Iris-setosa	0.331547843586981	0.445493393013231	0.222958763399788
25	5.0	3.5	1.3	0.3	Iris-setosa	0.340833390343682	0.435641864359966	0.223524745296351
26	5.1	3.8	1.9	0.4	Iris-setosa	0.329312613702523	0.452000463116693	0.218686923180784
27	5.4	3.4	1.7	0.2	Iris-setosa	0.315823889125293	0.464376710181294	0.219799400693413
28	5.5	2.5	4.0	1.3	Iris-versicolor	0.131195612330976	0.75830992775985	0.110494459909174
29	5.7	2.6	3.5	1.0	Iris-versicolor	0.172052086877016	0.690127545956273	0.137820367166711
30	5.8	2.6	4.0	1.2	Iris-versicolor	0.0879552677045265	0.83428540810243	0.0777593241930438
31	5.8	2.7	3.9	1.2	Iris-versicolor	0.098689293752253	0.816740432658629	0.0845702735891178
32	5.8	4.0	1.2	0.2	Iris-setosa	0.331757287054509	0.438127413866187	0.230115299079304
33	6.1	2.9	4.7	1.4	Iris-versicolor	0.108137402935531	0.77179296409432	0.120069632970149
34	6.2	2.8	4.8	1.8	Iris-virginica	0.153692822268654	0.678510173431695	0.167797004299651
35	6.3	2.3	4.4	1.3	Iris-versicolor	0.123318182636496	0.7496704236032	0.127011393760304
36	6.3	2.5	5.0	1.9	Iris-virginica	0.178728129188386	0.61280621385623	0.208465656955384
37	6.3	3.3	4.7	1.6	Iris-versicolor	0.160504671249814	0.663484373320946	0.176010955429239
38	6.4	3.2	5.3	2.3	Iris-virginica	0.2250405961301	0.487045280313397	0.287914123556503
39	6.5	3.0	5.8	2.2	Iris-virginica	0.213172274496556	0.43449324339262	0.352334482110825
40	6.7	3.0	5.2	2.3	Iris-virginica	0.222277870514573	0.50410506134991	0.273617068135517
41	6.7	3.1	5.6	2.4	Iris-virginica	0.227475716095996	0.442928693991263	0.329595589912741
42	6.7	3.3	5.7	2.5	Iris-virginica	0.233372055666568	0.415545069038955	0.351082875294477
43	6.8	2.8	4.8	1.4	Iris-versicolor	0.164828101724373	0.637907019922124	0.197264878353503
44	6.8	3.0	5.5	2.1	Iris-virginica	0.212534782206197	0.480358308031206	0.307106909762597
45	7.3	2.9	6.3	1.8	Iris-virginica	0.206817869407129	0.397004197710809	0.396177932882061
46	7.4	2.8	6.1	1.9	Iris-virginica	0.213097712173066	0.416652021995486	0.370250265831448
47	7.7	2.8	6.7	2.0	Iris-virginica	0.215299334956071	0.366493281107008	0.418207383936921
48	7.7	3.8	6.7	2.2	Iris-virginica	0.219684255803621	0.337280825731979	0.4430349184644
49	3.3	4.5	5.6	7.8	Iris-setosa	0.430267758856667	0.270338784245628	0.299393456897704

Rows: 1-49 | 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.