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verticapy.machine_learning.vertica.ensemble.RandomForestClassifier.predict_proba

RandomForestClassifier.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	4.8	3.4	1.9	0.2	Iris-setosa	0.330469537378669	0.455676724679682	0.213853737941649
2	5.5	2.4	3.8	1.1	Iris-versicolor	0.165174307700333	0.708029038960601	0.126796653339067
3	5.7	3.0	4.2	1.2	Iris-versicolor	0.0941658713963158	0.827138951889673	0.0786951767140109
4	6.1	3.0	4.6	1.4	Iris-versicolor	0.0960373873426229	0.812139107395003	0.0918235052623736
5	6.4	2.9	4.3	1.3	Iris-versicolor	0.103224838268904	0.802986623912893	0.0937885378182029
6	7.7	3.8	6.7	2.2	Iris-virginica	0.22945529169175	0.356539574414424	0.414005133893826
7	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
8	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
9	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
10	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
11	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
12	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
13	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
14	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
15	4.6	3.1	1.5	0.2	Iris-setosa	0.342381566982444	0.440359199151411	0.217259233866145
16	5.0	3.4	1.6	0.4	Iris-setosa	0.339600912383583	0.449183456016584	0.211215631599833
17	5.0	3.6	1.4	0.2	Iris-setosa	0.342456904811397	0.439394443858866	0.218148651329737
18	5.1	3.5	1.4	0.3	Iris-setosa	0.341347210169551	0.443372822414029	0.215279967416419
19	5.6	2.8	4.9	2.0	Iris-virginica	0.198362125080787	0.621036629048086	0.180601245871127
20	5.7	2.5	5.0	2.0	Iris-virginica	0.196815240773748	0.613363891190055	0.189820868036197
21	5.7	2.6	3.5	1.0	Iris-versicolor	0.179547324885046	0.687498516139106	0.132954158975847
22	6.1	2.8	4.0	1.3	Iris-versicolor	0.0822974223449005	0.850797519493526	0.0669050581615738
23	6.3	2.5	5.0	1.9	Iris-virginica	0.178136814477248	0.637703478396039	0.184159707126713
24	6.3	2.8	5.1	1.5	Iris-virginica	0.157376760718275	0.662590525026714	0.180032714255011
25	6.7	2.5	5.8	1.8	Iris-virginica	0.206694191796301	0.499780813857404	0.293524994346295
26	6.8	3.0	5.5	2.1	Iris-virginica	0.218305010067896	0.506847556553919	0.274847433378185
27	6.9	3.1	5.1	2.3	Iris-virginica	0.230837101319607	0.525167887910879	0.243995010769514
28	7.6	3.0	6.6	2.1	Iris-virginica	0.224902115071736	0.386381974502017	0.388715910426247
29	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
30	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
31	3.3	4.5	5.6	7.8	Iris-setosa	0.431116319881883	0.271067024713611	0.297816655404506
32	4.3	4.7	9.6	1.8	Iris-virginica	0.187599687561581	0.207793540971821	0.604606771466598
33	4.3	4.7	9.6	1.8	Iris-virginica	0.187599687561581	0.207793540971821	0.604606771466598
34	4.3	4.7	9.6	1.8	Iris-virginica	0.187599687561581	0.207793540971821	0.604606771466598
35	4.3	4.7	9.6	1.8	Iris-virginica	0.187599687561581	0.207793540971821	0.604606771466598
36	5.7	2.8	4.1	1.3	Iris-versicolor	0.0831231893076723	0.850412893502287	0.0664639171900405
37	5.7	2.8	4.5	1.3	Iris-versicolor	0.0821912895793099	0.843253040805401	0.0745556696152896
38	6.0	2.2	4.0	1.0	Iris-versicolor	0.141237112392074	0.737046875935078	0.121716011672848
39	7.7	3.0	6.1	2.3	Iris-virginica	0.237094641943449	0.418434256533225	0.344471101523326
40	4.4	2.9	1.4	0.2	Iris-setosa	0.346857178870287	0.433807022568667	0.219335798561047
41	4.5	2.3	1.3	0.3	Iris-setosa	0.342640318242828	0.438693017421995	0.218666664335176
42	5.5	3.5	1.3	0.2	Iris-setosa	0.332828960203565	0.449879217854413	0.217291821942021
43	5.9	3.0	5.1	1.8	Iris-virginica	0.180672557071566	0.626169361736201	0.193158081192232
44	6.2	2.8	4.8	1.8	Iris-virginica	0.150146857542961	0.704323993754855	0.145529148702184
45	6.4	3.2	5.3	2.3	Iris-virginica	0.230673508576814	0.51258174256049	0.256744748862696
46	6.5	3.0	5.2	2.0	Iris-virginica	0.201780612362257	0.571478273038855	0.226741114598888
47	6.9	3.1	5.4	2.1	Iris-virginica	0.220577546677448	0.511013927707144	0.268408525615407
48	7.2	3.2	6.0	1.8	Iris-virginica	0.216865972273879	0.444376000360913	0.338758027365208
49	7.3	2.9	6.3	1.8	Iris-virginica	0.216113764652944	0.42220530546652	0.361680929880536
50	7.7	2.6	6.9	2.3	Iris-virginica	0.229207946525538	0.372250699395398	0.398541354079064

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.