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verticapy.machine_learning.vertica.tree.DecisionTreeClassifier.predict

DecisionTreeClassifier.predict(vdf: str | vDataFrame, X: str | list[str] | None = None, name: str | None = None, cutoff: int | float | Decimal | 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	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(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	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
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	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	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
13	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
14	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
15	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
16	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
17	4.4	3.2	1.3	0.2	Iris-setosa	Iris-versicolor
18	4.6	3.4	1.4	0.3	Iris-setosa	Iris-versicolor
19	4.8	3.4	1.9	0.2	Iris-setosa	Iris-versicolor
20	4.9	3.1	1.5	0.1	Iris-setosa	Iris-versicolor
21	5.0	2.0	3.5	1.0	Iris-versicolor	Iris-versicolor
22	5.0	3.0	1.6	0.2	Iris-setosa	Iris-versicolor
23	5.0	3.2	1.2	0.2	Iris-setosa	Iris-versicolor
24	5.0	3.3	1.4	0.2	Iris-setosa	Iris-versicolor
25	5.0	3.4	1.6	0.4	Iris-setosa	Iris-versicolor
26	5.0	3.6	1.4	0.2	Iris-setosa	Iris-versicolor
27	5.1	3.8	1.9	0.4	Iris-setosa	Iris-versicolor
28	5.2	3.5	1.5	0.2	Iris-setosa	Iris-versicolor
29	5.5	2.4	3.7	1.0	Iris-versicolor	Iris-versicolor
30	5.6	2.5	3.9	1.1	Iris-versicolor	Iris-versicolor
31	5.9	3.0	4.2	1.5	Iris-versicolor	Iris-versicolor
32	6.0	2.2	5.0	1.5	Iris-virginica	Iris-versicolor
33	6.1	3.0	4.9	1.8	Iris-virginica	Iris-versicolor
34	6.2	2.2	4.5	1.5	Iris-versicolor	Iris-versicolor
35	6.2	2.9	4.3	1.3	Iris-versicolor	Iris-versicolor
36	6.3	2.5	4.9	1.5	Iris-versicolor	Iris-versicolor
37	6.4	2.7	5.3	1.9	Iris-virginica	Iris-versicolor
38	6.4	2.9	4.3	1.3	Iris-versicolor	Iris-versicolor
39	6.5	2.8	4.6	1.5	Iris-versicolor	Iris-versicolor
40	6.5	3.0	5.2	2.0	Iris-virginica	Iris-versicolor
41	6.5	3.2	5.1	2.0	Iris-virginica	Iris-versicolor
42	6.7	3.0	5.0	1.7	Iris-versicolor	Iris-versicolor
43	6.7	3.0	5.2	2.3	Iris-virginica	Iris-versicolor
44	6.9	3.2	5.7	2.3	Iris-virginica	Iris-versicolor
45	7.2	3.0	5.8	1.6	Iris-virginica	Iris-versicolor
46	7.7	3.8	6.7	2.2	Iris-virginica	Iris-virginica
47	7.9	3.8	6.4	2.0	Iris-virginica	Iris-virginica
48	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
49	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
50	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa

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.