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verticapy.machine_learning.vertica.ensemble.XGBClassifier.predict

XGBClassifier.predict(vdf: Annotated[str | vDataFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str | None = None, cutoff: Annotated[int | float | Decimal, 'Python Numbers'] | None = None, inplace: bool = True) → vDataFrame

Predicts 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.

cutoff: PythonNumber, optional

Cutoff for which the tested category is accepted as a prediction. This parameter is only used for binary classification.

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(test, name = "prediction"

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)	Abc prediction Varchar(15)
1	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
2	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
3	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
4	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
5	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
6	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
7	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
8	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
9	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
10	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
11	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
12	4.5	2.3	1.3	0.3	Iris-setosa	Iris-versicolor
13	4.6	3.1	1.5	0.2	Iris-setosa	Iris-versicolor
14	4.9	2.4	3.3	1.0	Iris-versicolor	Iris-versicolor
15	5.0	2.3	3.3	1.0	Iris-versicolor	Iris-versicolor
16	5.0	3.4	1.5	0.2	Iris-setosa	Iris-versicolor
17	5.0	3.5	1.3	0.3	Iris-setosa	Iris-versicolor
18	5.0	3.6	1.4	0.2	Iris-setosa	Iris-versicolor
19	5.1	3.4	1.5	0.2	Iris-setosa	Iris-versicolor
20	5.1	3.8	1.6	0.2	Iris-setosa	Iris-versicolor
21	5.1	3.8	1.9	0.4	Iris-setosa	Iris-versicolor
22	5.2	4.1	1.5	0.1	Iris-setosa	Iris-versicolor
23	5.5	3.5	1.3	0.2	Iris-setosa	Iris-versicolor
24	5.5	4.2	1.4	0.2	Iris-setosa	Iris-versicolor
25	5.6	2.5	3.9	1.1	Iris-versicolor	Iris-versicolor
26	5.6	2.8	4.9	2.0	Iris-virginica	Iris-versicolor
27	5.7	2.8	4.5	1.3	Iris-versicolor	Iris-versicolor
28	5.8	2.6	4.0	1.2	Iris-versicolor	Iris-versicolor
29	5.8	4.0	1.2	0.2	Iris-setosa	Iris-versicolor
30	5.9	3.0	5.1	1.8	Iris-virginica	Iris-versicolor
31	6.0	2.7	5.1	1.6	Iris-versicolor	Iris-versicolor
32	6.1	2.8	4.0	1.3	Iris-versicolor	Iris-versicolor
33	6.1	3.0	4.6	1.4	Iris-versicolor	Iris-versicolor
34	6.3	2.5	5.0	1.9	Iris-virginica	Iris-versicolor
35	6.3	3.3	6.0	2.5	Iris-virginica	Iris-virginica
36	6.3	3.4	5.6	2.4	Iris-virginica	Iris-virginica
37	6.4	2.8	5.6	2.2	Iris-virginica	Iris-versicolor
38	6.4	2.9	4.3	1.3	Iris-versicolor	Iris-versicolor
39	6.6	3.0	4.4	1.4	Iris-versicolor	Iris-versicolor
40	6.7	3.3	5.7	2.1	Iris-virginica	Iris-virginica
41	7.1	3.0	5.9	2.1	Iris-virginica	Iris-virginica
42	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
43	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
44	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica

Rows: 1-44 | Columns: 6

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.