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verticapy.machine_learning.vertica.tree.DecisionTreeClassifier.predict_proba

DecisionTreeClassifier.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	0.443076519884209	0.264371284114223	0.292552196001569
2	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
3	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
4	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
5	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
6	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
7	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
8	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
9	4.3	3.0	1.1	0.1	Iris-setosa	0.33754419360404	0.42672768468528	0.235728121710679
10	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
11	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
12	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
13	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
14	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
15	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
16	4.4	3.0	1.3	0.2	Iris-setosa	0.333776572206532	0.434050963947607	0.232172463845861
17	4.8	3.4	1.6	0.2	Iris-setosa	0.321797171014942	0.449324118681596	0.228878710303462
18	4.8	3.4	1.9	0.2	Iris-setosa	0.312885151328043	0.460446403957884	0.226668444714073
19	4.9	3.1	1.5	0.1	Iris-setosa	0.316078666910227	0.454432022497724	0.229489310592049
20	5.0	3.2	1.2	0.2	Iris-setosa	0.325118289199046	0.444533986279189	0.230347724521764
21	5.0	3.4	1.6	0.4	Iris-setosa	0.321586976938027	0.454257240511204	0.22415578255077
22	5.2	3.5	1.5	0.2	Iris-setosa	0.31653436800669	0.454833583575458	0.228632048417852
23	5.4	3.0	4.5	1.5	Iris-versicolor	0.146445222053144	0.707129824507175	0.146424953439682
24	5.4	3.9	1.3	0.4	Iris-setosa	0.328336101638382	0.441439141003298	0.230224757358321
25	5.4	3.9	1.7	0.4	Iris-setosa	0.317128385901179	0.456044649246611	0.226826964852209
26	5.5	2.4	3.7	1.0	Iris-versicolor	0.16289279356446	0.692746199202369	0.144361007233171
27	5.5	2.5	4.0	1.3	Iris-versicolor	0.127697699943387	0.757045731856535	0.115256568200078
28	5.5	3.5	1.3	0.2	Iris-setosa	0.316043017237286	0.453812905534609	0.230144077228105
29	5.5	4.2	1.4	0.2	Iris-setosa	0.323746655516299	0.440251937495442	0.236001406988259
30	5.6	2.7	4.2	1.3	Iris-versicolor	0.0860226920927532	0.831035040413682	0.0829422674935643
31	5.7	4.4	1.5	0.4	Iris-setosa	0.325331819935966	0.439485700575589	0.235182479488445
32	5.8	2.7	4.1	1.0	Iris-versicolor	0.0888496042650257	0.821814505365276	0.0893358903696982
33	5.8	2.7	5.1	1.9	Iris-virginica	0.180114418785807	0.593480728240582	0.226404852973611
34	6.0	2.2	4.0	1.0	Iris-versicolor	0.131358604154032	0.737118511583579	0.131522884262389
35	6.0	2.9	4.5	1.5	Iris-versicolor	0.0780001901662964	0.83806754880409	0.0839322610296137
36	6.1	2.9	4.7	1.4	Iris-versicolor	0.103736558245416	0.772824775628286	0.123438666126298
37	6.1	3.0	4.6	1.4	Iris-versicolor	0.0975886032926231	0.790475969490157	0.11193542721722
38	6.2	3.4	5.4	2.3	Iris-virginica	0.223778397812259	0.454535288902444	0.321686313285298
39	6.4	2.8	5.6	2.2	Iris-virginica	0.206538141236294	0.46734617301262	0.326115685751086
40	6.4	2.9	4.3	1.3	Iris-versicolor	0.0998628117565708	0.792531947330753	0.107605240912676
41	6.4	3.1	5.5	1.8	Iris-virginica	0.188242323393208	0.502367125810959	0.309390550795833
42	6.5	3.0	5.2	2.0	Iris-virginica	0.192308723301857	0.543075158306342	0.264616118391801
43	6.7	3.1	4.4	1.4	Iris-versicolor	0.148043174459245	0.68580926069134	0.166147564849415
44	6.7	3.3	5.7	2.1	Iris-virginica	0.206765084598069	0.4338669472077	0.359367968194231
45	6.7	3.3	5.7	2.5	Iris-virginica	0.22626277113191	0.409901880778792	0.363835348089299
46	7.1	3.0	5.9	2.1	Iris-virginica	0.207968287336359	0.420024398329029	0.372007314334612
47	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
48	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129
49	3.3	4.5	5.6	7.8	Iris-setosa	0.443076519884209	0.264371284114223	0.292552196001569
50	4.3	4.7	9.6	1.8	Iris-virginica	0.215245230157469	0.235850873393401	0.548903896449129

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.