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verticapy.machine_learning.vertica.ensemble.RandomForestClassifier.predict_proba

RandomForestClassifier.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	0.441132169290149	0.264799884163124	0.294067946546727
2	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
3	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
4	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
5	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
6	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
7	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
8	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
9	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
10	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
11	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
12	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
13	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
14	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
15	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
16	4.4	3.2	1.3	0.2	Iris-setosa	0.339136251899164	0.433191830971686	0.22767191712915
17	4.6	3.2	1.4	0.2	Iris-setosa	0.332158302920315	0.44231589427987	0.225525802799815
18	4.6	3.4	1.4	0.3	Iris-setosa	0.337621831419268	0.437652214023102	0.22472595455763
19	4.7	3.2	1.6	0.2	Iris-setosa	0.324457484387066	0.452334438430807	0.223208077182127
20	4.8	3.1	1.6	0.2	Iris-setosa	0.320420489593969	0.457521411857015	0.222058098549016
21	4.9	2.5	4.5	1.7	Iris-virginica	0.228647163322769	0.574181053286686	0.197171783390545
22	4.9	3.0	1.4	0.2	Iris-setosa	0.322320865164344	0.454652909538866	0.223026225296791
23	4.9	3.1	1.5	0.1	Iris-setosa	0.318628337223008	0.457219447719586	0.224152215057406
24	5.1	3.3	1.7	0.5	Iris-setosa	0.319705394846545	0.465575659866934	0.214718945286521
25	5.1	3.4	1.5	0.2	Iris-setosa	0.320209541888799	0.456856171916536	0.222934286194664
26	5.2	2.7	3.9	1.4	Iris-versicolor	0.181583837047889	0.675884326852252	0.14253183609986
27	5.4	3.0	4.5	1.5	Iris-versicolor	0.157246311073659	0.693342021653688	0.149411667272653
28	5.4	3.7	1.5	0.2	Iris-setosa	0.318167205705733	0.457197959799855	0.224634834494412
29	5.5	2.5	4.0	1.3	Iris-versicolor	0.127507300688574	0.762395194944863	0.110097504366563
30	5.6	2.5	3.9	1.1	Iris-versicolor	0.123441689358615	0.767623103452985	0.1089352071884
31	5.6	2.8	4.9	2.0	Iris-virginica	0.20107654188303	0.589757990984653	0.209165467132317
32	5.7	4.4	1.5	0.4	Iris-setosa	0.329056365158644	0.44078084604741	0.230162788793946
33	5.8	2.7	5.1	1.9	Iris-virginica	0.188692683535117	0.586327217306831	0.224980099158051
34	5.8	2.7	5.1	1.9	Iris-virginica	0.188692683535117	0.586327217306831	0.224980099158051
35	5.9	3.2	4.8	1.8	Iris-versicolor	0.177309090143742	0.631291090168402	0.191399819687856
36	6.0	3.4	4.5	1.6	Iris-versicolor	0.161866754666759	0.676011003842224	0.162122241491017
37	6.1	2.9	4.7	1.4	Iris-versicolor	0.111724423800149	0.761676834191808	0.126598742008043
38	6.2	3.4	5.4	2.3	Iris-virginica	0.232146059833019	0.452017964664642	0.315835975502339
39	6.3	2.5	4.9	1.5	Iris-versicolor	0.144521294027869	0.680494910547347	0.174983795424784
40	6.3	2.7	4.9	1.8	Iris-virginica	0.163343081645095	0.646117989394385	0.190538928960521
41	6.3	2.8	5.1	1.5	Iris-virginica	0.158530046724235	0.631621793846798	0.209848159428967
42	6.3	3.3	4.7	1.6	Iris-versicolor	0.161867939752811	0.656664985221293	0.181467075025896
43	6.3	3.3	6.0	2.5	Iris-virginica	0.224119278177279	0.366033454489196	0.409847267333526
44	6.5	3.0	5.8	2.2	Iris-virginica	0.212760096207817	0.4287125216239	0.358527382168283
45	6.7	3.0	5.2	2.3	Iris-virginica	0.221556408968857	0.500244380317513	0.27819921071363
46	6.7	3.3	5.7	2.1	Iris-virginica	0.213191850171386	0.434241361180963	0.352566788647651
47	6.9	3.1	5.4	2.1	Iris-virginica	0.2138855024258	0.481946379440297	0.304168118133903
48	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727
49	4.3	4.7	9.6	1.8	Iris-virginica	0.210413571055425	0.22897029965079	0.560616129293785
50	3.3	4.5	5.6	7.8	Iris-setosa	0.441132169290149	0.264799884163124	0.294067946546727

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.