Feedback

×

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

verticapy.machine_learning.vertica.tree.DummyTreeClassifier.predict_proba

DummyTreeClassifier.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	0.434554856074108	0.267163002207235	0.298282141718658
2	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
3	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
4	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
5	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
6	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
7	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
8	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
9	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
10	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
11	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
12	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
13	4.3	4.7	9.6	1.8	Iris-virginica	0.211115179742846	0.229094755265238	0.559790064991917
14	4.6	3.2	1.4	0.2	Iris-setosa	0.335349569792779	0.44070592091739	0.223944509289831
15	4.6	3.4	1.4	0.3	Iris-setosa	0.341005236084375	0.43587624647061	0.223118517445015
16	4.6	3.6	1.0	0.2	Iris-setosa	0.348930843579513	0.422170307416018	0.228898849004469
17	4.9	3.1	1.5	0.1	Iris-setosa	0.321563673217671	0.455873056949939	0.22256326983239
18	5.0	3.4	1.6	0.4	Iris-setosa	0.327998225872555	0.454885407147219	0.217116366980226
19	5.1	3.8	1.9	0.4	Iris-setosa	0.32352236849884	0.458704406641455	0.217773224859705
20	5.2	3.4	1.4	0.2	Iris-setosa	0.323836989152208	0.454176474581286	0.221986536266507
21	5.3	3.7	1.5	0.2	Iris-setosa	0.32351897670127	0.453239866901135	0.223241156397595
22	5.4	3.0	4.5	1.5	Iris-versicolor	0.160961706062354	0.687174634743214	0.151863659194432
23	5.5	2.3	4.0	1.3	Iris-versicolor	0.139969571750342	0.739562373001549	0.120468055248109
24	5.5	2.6	4.4	1.2	Iris-versicolor	0.116081485004086	0.769870782165711	0.114047732830203
25	5.6	3.0	4.1	1.3	Iris-versicolor	0.110192548200212	0.793441789172921	0.0963656626268668
26	5.6	3.0	4.5	1.5	Iris-versicolor	0.135154287768984	0.733915749846648	0.130929962384368
27	5.7	2.8	4.5	1.3	Iris-versicolor	0.0960772470541657	0.805891653802757	0.098031099143077
28	5.7	2.9	4.2	1.3	Iris-versicolor	0.0767965981313851	0.852754148455068	0.0704492534135472
29	5.7	3.8	1.7	0.3	Iris-setosa	0.312449619765785	0.467049463575203	0.220500916659013
30	5.9	3.0	4.2	1.5	Iris-versicolor	0.0889909788573198	0.830774240972409	0.0802347801702709
31	5.9	3.0	5.1	1.8	Iris-virginica	0.188603919415595	0.577582737817031	0.233813342767374
32	6.0	2.7	5.1	1.6	Iris-versicolor	0.167244718643586	0.619499057086828	0.213256224269586
33	6.0	3.4	4.5	1.6	Iris-versicolor	0.168262475293007	0.66452129171856	0.167216232988432
34	6.2	2.9	4.3	1.3	Iris-versicolor	0.07781701739542	0.844225113135955	0.077957869468625
35	6.3	2.3	4.4	1.3	Iris-versicolor	0.117928299533607	0.758878350054132	0.123193350412261
36	6.3	2.8	5.1	1.5	Iris-virginica	0.164641029591045	0.618018449011949	0.217340521397006
37	6.3	3.3	4.7	1.6	Iris-versicolor	0.168711943339477	0.643321801361348	0.187966255299175
38	6.3	3.4	5.6	2.4	Iris-virginica	0.235423486477369	0.411380994459193	0.353195519063438
39	6.4	2.7	5.3	1.9	Iris-virginica	0.195295862989045	0.538662427643254	0.266041709367702
40	6.4	3.2	5.3	2.3	Iris-virginica	0.229147871657942	0.47265275128505	0.298199377057007
41	6.5	2.8	4.6	1.5	Iris-versicolor	0.138834629747788	0.707871816783524	0.153293553468688
42	6.5	3.0	5.5	1.8	Iris-virginica	0.198073532546373	0.495944687819377	0.30598177963425
43	6.5	3.0	5.8	2.2	Iris-virginica	0.215245789216114	0.421469388990653	0.363284821793232
44	6.6	2.9	4.6	1.3	Iris-versicolor	0.142382186918272	0.69500733912999	0.162610473951738
45	6.8	3.2	5.9	2.3	Iris-virginica	0.220516502438304	0.397013955376408	0.382469542185288
46	7.2	3.2	6.0	1.8	Iris-virginica	0.208392709089386	0.408302090067738	0.383305200842875
47	7.2	3.6	6.1	2.5	Iris-virginica	0.230667000887573	0.359791329428134	0.409541669684292
48	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
49	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658
50	3.3	4.5	5.6	7.8	Iris-setosa	0.434554856074108	0.267163002207235	0.298282141718658

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.