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verticapy.machine_learning.vertica.tree.DecisionTreeClassifier.predict_proba

DecisionTreeClassifier.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	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_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	4.9	3.1	1.5	0.1	Iris-setosa	0.321793271364518	0.450417128751775	0.227789599883707
2	4.9	3.1	1.5	0.1	Iris-setosa	0.321793271364518	0.450417128751775	0.227789599883707
3	5.0	2.3	3.3	1.0	Iris-versicolor	0.238208504367675	0.580398017139699	0.181393478492626
4	5.1	3.5	1.4	0.2	Iris-setosa	0.327795169835482	0.444027195004436	0.228177635160082
5	5.4	3.4	1.5	0.4	Iris-setosa	0.321441215664116	0.455972084962345	0.222586699373538
6	6.3	3.3	6.0	2.5	Iris-virginica	0.218088822463803	0.366245036092101	0.415666141444096
7	6.4	2.9	4.3	1.3	Iris-versicolor	0.109608841970972	0.775047271335912	0.115343886693116
8	6.5	3.0	5.5	1.8	Iris-virginica	0.189940779953225	0.506706119717108	0.303353100329666
9	6.7	3.0	5.0	1.7	Iris-versicolor	0.178342805810446	0.589112707892134	0.23254448629742
10	7.0	3.2	4.7	1.4	Iris-versicolor	0.183111048925534	0.59196512899543	0.224923822079036
11	7.7	3.8	6.7	2.2	Iris-virginica	0.215077580116424	0.331221991281147	0.453700428602428
12	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
13	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
14	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
15	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
16	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
17	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
18	4.9	2.4	3.3	1.0	Iris-versicolor	0.24517170018064	0.570963172696731	0.183865127122628
19	5.1	3.5	1.4	0.3	Iris-setosa	0.330237929989614	0.443331418845604	0.226430651164782
20	5.1	3.8	1.6	0.2	Iris-setosa	0.32706153036859	0.44383783647166	0.22910063315975
21	6.0	3.4	4.5	1.6	Iris-versicolor	0.156700283138456	0.682252613502512	0.161047103359032
22	6.4	2.7	5.3	1.9	Iris-virginica	0.186230740266408	0.551886547352034	0.261882712381558
23	7.4	2.8	6.1	1.9	Iris-virginica	0.208541247627729	0.411300090084841	0.38015866228743
24	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
25	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
26	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
27	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
28	3.3	4.5	5.6	7.8	Iris-setosa	0.436338570066003	0.267636258982626	0.29602517095137
29	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
30	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
31	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
32	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
33	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
34	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
35	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
36	5.1	2.5	3.0	1.1	Iris-versicolor	0.256212483646926	0.562778411407587	0.181009104945487
37	5.1	3.7	1.5	0.4	Iris-setosa	0.33340096278031	0.441260352760515	0.225338684459175
38	5.4	3.4	1.7	0.2	Iris-setosa	0.310465519396302	0.465464529472506	0.224069951131192
39	5.8	2.7	4.1	1.0	Iris-versicolor	0.0893551061167686	0.822982222574119	0.0876626713091128
40	6.0	2.2	4.0	1.0	Iris-versicolor	0.133200466284938	0.736397461292719	0.130402072422343
41	6.1	2.9	4.7	1.4	Iris-versicolor	0.105701546239679	0.771319998178445	0.122978455581876
42	4.3	4.7	9.6	1.8	Iris-virginica	0.21335498727923	0.235562582870007	0.551082429850763
43	4.4	2.9	1.4	0.2	Iris-setosa	0.335837188564626	0.43476606183372	0.229396749601654
44	5.5	3.5	1.3	0.2	Iris-setosa	0.322305578415309	0.448924083158163	0.228770338426528
45	5.8	2.7	5.1	1.9	Iris-virginica	0.179344969364638	0.599349218630275	0.221305812005087
46	5.9	3.0	4.2	1.5	Iris-versicolor	0.0795947094988028	0.846243388114705	0.0741619023864923
47	6.2	2.9	4.3	1.3	Iris-versicolor	0.0792550839055729	0.838840990506825	0.0819039255876025
48	6.4	3.2	5.3	2.3	Iris-virginica	0.220263990403998	0.483513513487816	0.296222496108186
49	6.6	2.9	4.6	1.3	Iris-versicolor	0.139692009473713	0.695941213047516	0.164366777478771
50	6.9	3.1	4.9	1.5	Iris-versicolor	0.180072118324324	0.586603608734552	0.233324272941124

Rows: 1-50 | 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.