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verticapy.machine_learning.vertica.neighbors.KNeighborsClassifier.predict

KNeighborsClassifier.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	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(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	5.0	2.3	3.3	1.0	Iris-versicolor	Iris-versicolor
2	5.8	2.8	5.1	2.4	Iris-virginica	Iris-versicolor
3	5.9	3.2	4.8	1.8	Iris-versicolor	Iris-versicolor
4	6.3	3.3	4.7	1.6	Iris-versicolor	Iris-versicolor
5	6.3	3.3	6.0	2.5	Iris-virginica	Iris-virginica
6	6.8	3.2	5.9	2.3	Iris-virginica	Iris-versicolor
7	7.0	3.2	4.7	1.4	Iris-versicolor	Iris-versicolor
8	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
10	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
11	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
12	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
13	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
14	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
15	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
16	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
17	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
18	4.6	3.1	1.5	0.2	Iris-setosa	Iris-versicolor
19	4.9	2.5	4.5	1.7	Iris-virginica	Iris-versicolor
20	5.0	3.2	1.2	0.2	Iris-setosa	Iris-versicolor
21	5.0	3.6	1.4	0.2	Iris-setosa	Iris-versicolor
22	5.1	3.8	1.6	0.2	Iris-setosa	Iris-versicolor
23	7.4	2.8	6.1	1.9	Iris-virginica	Iris-versicolor
24	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
25	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
26	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
27	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
28	5.2	3.5	1.5	0.2	Iris-setosa	Iris-versicolor
29	6.1	2.8	4.7	1.2	Iris-versicolor	Iris-versicolor
30	6.2	2.9	4.3	1.3	Iris-versicolor	Iris-versicolor
31	6.3	2.3	4.4	1.3	Iris-versicolor	Iris-versicolor
32	6.5	3.0	5.2	2.0	Iris-virginica	Iris-versicolor
33	6.6	3.0	4.4	1.4	Iris-versicolor	Iris-versicolor
34	6.9	3.1	5.4	2.1	Iris-virginica	Iris-versicolor
35	7.2	3.2	6.0	1.8	Iris-virginica	Iris-versicolor
36	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
37	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
38	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
39	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
40	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
41	5.1	2.5	3.0	1.1	Iris-versicolor	Iris-versicolor
42	5.1	3.7	1.5	0.4	Iris-setosa	Iris-versicolor
43	5.1	3.8	1.5	0.3	Iris-setosa	Iris-versicolor
44	5.4	3.4	1.7	0.2	Iris-setosa	Iris-versicolor
45	5.4	3.7	1.5	0.2	Iris-setosa	Iris-versicolor
46	5.5	4.2	1.4	0.2	Iris-setosa	Iris-versicolor
47	6.0	3.0	4.8	1.8	Iris-virginica	Iris-versicolor
48	6.4	2.8	5.6	2.1	Iris-virginica	Iris-versicolor
49	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
50	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica

Rows: 1-50 | 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.