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verticapy.machine_learning.vertica.naive_bayes.NaiveBayes.predict_proba

NaiveBayes.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	0.442411484732701	0.263294596073674	0.294293919193625
2	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
3	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
4	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
5	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
6	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
7	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
8	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625
9	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
10	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
11	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
12	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
13	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
14	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
15	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
16	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
17	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
18	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
19	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
20	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
21	4.3	4.7	9.6	1.8	Iris-virginica	0.225441334110268	0.246441761396607	0.528116904493125
22	4.5	2.3	1.3	0.3	Iris-setosa	0.323499478700682	0.441310888698467	0.235189632600851
23	4.7	3.2	1.3	0.2	Iris-setosa	0.326427049487265	0.438142863335904	0.23543008717683
24	4.7	3.2	1.6	0.2	Iris-setosa	0.318642690815432	0.448139060113396	0.233218249071172
25	4.8	3.0	1.4	0.3	Iris-setosa	0.321345067959777	0.446763021589693	0.23189191045053
26	4.8	3.1	1.6	0.2	Iris-setosa	0.314733182771425	0.453090886795924	0.232175930432651
27	4.8	3.4	1.6	0.2	Iris-setosa	0.319049029735773	0.447310881958478	0.23364008830575
28	4.9	2.5	4.5	1.7	Iris-virginica	0.21749981907088	0.572836037842085	0.209664143087035
29	4.9	3.0	1.4	0.2	Iris-setosa	0.31677608715263	0.450175608029865	0.233048304817505
30	5.0	3.0	1.6	0.2	Iris-setosa	0.308458548332792	0.460841608394971	0.230699843272237
31	5.0	3.4	1.6	0.4	Iris-setosa	0.31886475938449	0.452058227468672	0.229077013146838
32	5.1	2.5	3.0	1.1	Iris-versicolor	0.247551326609843	0.563969422113266	0.18847925127689
33	5.2	3.5	1.5	0.2	Iris-setosa	0.313986408177604	0.452531445150084	0.233482146672312
34	5.3	3.7	1.5	0.2	Iris-setosa	0.314983527286092	0.450079381615815	0.234937091098093
35	5.4	3.7	1.5	0.2	Iris-setosa	0.312874410294837	0.452277437885989	0.234848151819174
36	5.6	3.0	4.5	1.5	Iris-versicolor	0.119256800428704	0.749429211992299	0.131313987578997
37	5.7	2.8	4.1	1.3	Iris-versicolor	0.0704266173155149	0.858914817180341	0.070658565504144
38	5.7	2.9	4.2	1.3	Iris-versicolor	0.0703623931009734	0.856804200572242	0.0728334063267846
39	6.0	2.2	4.0	1.0	Iris-versicolor	0.126455973065416	0.73964047475508	0.133903552179504
40	6.0	2.9	4.5	1.5	Iris-versicolor	0.0823124766219425	0.822617198432271	0.0950703249457867
41	6.1	2.8	4.7	1.2	Iris-versicolor	0.0997551751515989	0.769633610186828	0.130611214661573
42	6.1	3.0	4.6	1.4	Iris-versicolor	0.100382368104081	0.775899493244614	0.123718138651305
43	6.3	2.9	5.6	1.8	Iris-virginica	0.181682183533247	0.475414261457665	0.342903555009088
44	6.4	2.8	5.6	2.2	Iris-virginica	0.200476502600162	0.449237739099193	0.350285758300645
45	6.4	3.2	4.5	1.5	Iris-versicolor	0.137315433053186	0.69811962224299	0.164564944703824
46	6.7	3.0	5.0	1.7	Iris-versicolor	0.172753627217845	0.578330098621114	0.24891627416104
47	7.2	3.0	5.8	1.6	Iris-virginica	0.191735872102377	0.435746499423841	0.372517628473782
48	7.7	2.8	6.7	2.0	Iris-virginica	0.203655270918081	0.349132046262073	0.447212682819846
49	7.9	3.8	6.4	2.0	Iris-virginica	0.214266698180343	0.347693141487033	0.438040160332624
50	3.3	4.5	5.6	7.8	Iris-setosa	0.442411484732701	0.263294596073674	0.294293919193625

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.