Feedback

×

• Feedback
• Was this page helpful?*
  • Yes
  • No
• Do you have any additional feedback?
  • Yes
  • No
• Additional Feedback

verticapy.machine_learning.vertica.neighbors.KNeighborsClassifier.predict_proba

KNeighborsClassifier.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	0.417269007598825	0.275512012636973	0.307218979764202
2	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
3	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
4	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
5	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
6	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
7	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
8	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
9	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
10	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
11	4.3	4.7	9.6	1.8	Iris-virginica	0.2041346829177	0.22680859112236	0.569056725959939
12	4.3	4.7	9.6	1.8	Iris-virginica	0.2041346829177	0.22680859112236	0.569056725959939
13	4.3	4.7	9.6	1.8	Iris-virginica	0.2041346829177	0.22680859112236	0.569056725959939
14	4.3	4.7	9.6	1.8	Iris-virginica	0.2041346829177	0.22680859112236	0.569056725959939
15	4.3	4.7	9.6	1.8	Iris-virginica	0.2041346829177	0.22680859112236	0.569056725959939
16	4.3	4.7	9.6	1.8	Iris-virginica	0.2041346829177	0.22680859112236	0.569056725959939
17	4.8	3.4	1.9	0.2	Iris-setosa	0.337516068481235	0.447480233907883	0.215003697610882
18	4.9	3.1	1.5	0.1	Iris-setosa	0.339371870917301	0.442318758474899	0.218309370607801
19	5.0	3.3	1.4	0.2	Iris-setosa	0.345070279347704	0.437310473586596	0.2176192470657
20	5.0	3.5	1.3	0.3	Iris-setosa	0.353346330195438	0.428912391583282	0.21774127822128
21	5.1	3.8	1.5	0.3	Iris-setosa	0.350650220379872	0.431364382963014	0.217985396657114
22	5.1	3.8	1.9	0.4	Iris-setosa	0.342432681806733	0.44483190152129	0.212735416671976
23	5.2	2.7	3.9	1.4	Iris-versicolor	0.191113897770759	0.673076389107769	0.135809713121472
24	5.2	4.1	1.5	0.1	Iris-setosa	0.347048219419152	0.428210022344237	0.224741758236611
25	5.7	2.8	4.1	1.3	Iris-versicolor	0.0719641715144358	0.868768617242134	0.0592672112434307
26	5.7	4.4	1.5	0.4	Iris-setosa	0.349050230805632	0.427185779034238	0.22376399016013
27	5.8	2.6	4.0	1.2	Iris-versicolor	0.0879847109699848	0.838990471129355	0.07302481790066
28	6.0	2.2	4.0	1.0	Iris-versicolor	0.141228331749341	0.733553165125063	0.125218503125596
29	6.0	2.2	5.0	1.5	Iris-virginica	0.168796864933507	0.638640685067479	0.192562449999014
30	6.0	3.4	4.5	1.6	Iris-versicolor	0.165832693056216	0.680707622667572	0.153459684276211
31	6.1	2.8	4.0	1.3	Iris-versicolor	0.0804837592616755	0.851988054540394	0.0675281861979308
32	6.1	2.8	4.7	1.2	Iris-versicolor	0.110222130641891	0.770695274891609	0.1190825944665
33	6.1	3.0	4.6	1.4	Iris-versicolor	0.109444369123682	0.779813024968336	0.110742605907982
34	6.2	2.2	4.5	1.5	Iris-versicolor	0.14338349251736	0.717131000190287	0.139485507292352
35	6.3	2.8	5.1	1.5	Iris-virginica	0.165172505583942	0.630380572285128	0.20444692213093
36	6.5	2.8	4.6	1.5	Iris-versicolor	0.142011644454632	0.711407370249923	0.146580985295445
37	6.5	3.0	5.8	2.2	Iris-virginica	0.218101156566025	0.434458290738227	0.347440552695748
38	6.6	2.9	4.6	1.3	Iris-versicolor	0.146934758345233	0.69621690825936	0.156848333395407
39	6.8	3.0	5.5	2.1	Iris-virginica	0.217905753857681	0.479073804534102	0.303020441608217
40	6.9	3.1	5.4	2.1	Iris-virginica	0.220720002058947	0.484094296021512	0.295185701919541
41	7.2	3.0	5.8	1.6	Iris-virginica	0.208939887244081	0.455911756498925	0.335148356256994
42	7.2	3.2	6.0	1.8	Iris-virginica	0.212987467755449	0.419294544725339	0.367717987519212
43	7.4	2.8	6.1	1.9	Iris-virginica	0.217657423418085	0.417704242325408	0.364638334256507
44	7.6	3.0	6.6	2.1	Iris-virginica	0.219223295257315	0.366726464108428	0.414050240634257
45	7.7	2.8	6.7	2.0	Iris-virginica	0.219138484418682	0.36855192185225	0.412309593729068
46	7.7	3.0	6.1	2.3	Iris-virginica	0.232883846650841	0.398727070276181	0.368389083072978
47	7.9	3.8	6.4	2.0	Iris-virginica	0.229089129867112	0.365612050711747	0.405298819421142
48	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
49	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202
50	3.3	4.5	5.6	7.8	Iris-setosa	0.417269007598825	0.275512012636973	0.307218979764202

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.