vastorbit.machine_learning.vast.ensemble.GradientBoostingClassifier.predict

GradientBoostingClassifier.predict(vdf: Annotated[str | VastFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str | None = None, cutoff: Annotated[int | float | Decimal, 'Python Numbers'] | None = None, inplace: bool = True) → VastFrame

Predicts 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 VastColumn. If empty, a name is generated.

• cutoff (PythonNumber, optional) – Cutoff for which the tested category is accepted as a prediction. This parameter is only used for binary classification.

• inplace (bool, optional) – If set to True, the prediction is added to the VastFrame.

Returns:

the input object.

Return type:

VastFrame

Examples

For this example, we will use the Iris dataset.

import vastorbit.datasets as vod
data = vod.load_iris()
train, test = data.train_test_split(test_size = 0.2)

	123 sepallengthcm Decimal(5, 2)	123 sepalwidthcm Decimal(5, 2)	123 petallengthcm Decimal(5, 2)	123 petalwidthcm Decimal(5, 2)	Abc species Varchar(30)
1	5.1	3.5	1.4	0.2	Iris-setosa
2	4.9	3.0	1.4	0.2	Iris-setosa
3	4.7	3.2	1.3	0.2	Iris-setosa
4	4.6	3.1	1.5	0.2	Iris-setosa
5	5.0	3.6	1.4	0.2	Iris-setosa
6	5.4	3.9	1.7	0.4	Iris-setosa
7	4.6	3.4	1.4	0.3	Iris-setosa
8	5.0	3.4	1.5	0.2	Iris-setosa
9	4.4	2.9	1.4	0.2	Iris-setosa
10	4.9	3.1	1.5	0.1	Iris-setosa
11	5.4	3.7	1.5	0.2	Iris-setosa
12	4.8	3.4	1.6	0.2	Iris-setosa
13	4.8	3.0	1.4	0.1	Iris-setosa
14	4.3	3.0	1.1	0.1	Iris-setosa
15	5.8	4.0	1.2	0.2	Iris-setosa
16	5.7	4.4	1.5	0.4	Iris-setosa
17	5.4	3.9	1.3	0.4	Iris-setosa
18	5.1	3.5	1.4	0.3	Iris-setosa
19	5.7	3.8	1.7	0.3	Iris-setosa
20	5.1	3.8	1.5	0.3	Iris-setosa
21	5.4	3.4	1.7	0.2	Iris-setosa
22	5.1	3.7	1.5	0.4	Iris-setosa
23	4.6	3.6	1.0	0.2	Iris-setosa
24	5.1	3.3	1.7	0.5	Iris-setosa
25	4.8	3.4	1.9	0.2	Iris-setosa
26	5.0	3.0	1.6	0.2	Iris-setosa
27	5.0	3.4	1.6	0.4	Iris-setosa
28	5.2	3.5	1.5	0.2	Iris-setosa
29	5.2	3.4	1.4	0.2	Iris-setosa
30	4.7	3.2	1.6	0.2	Iris-setosa
31	4.8	3.1	1.6	0.2	Iris-setosa
32	5.4	3.4	1.5	0.4	Iris-setosa
33	5.2	4.1	1.5	0.1	Iris-setosa
34	5.5	4.2	1.4	0.2	Iris-setosa
35	4.9	3.1	1.5	0.1	Iris-setosa
36	5.0	3.2	1.2	0.2	Iris-setosa
37	5.5	3.5	1.3	0.2	Iris-setosa
38	4.9	3.1	1.5	0.1	Iris-setosa
39	4.4	3.0	1.3	0.2	Iris-setosa
40	5.1	3.4	1.5	0.2	Iris-setosa
41	5.0	3.5	1.3	0.3	Iris-setosa
42	4.5	2.3	1.3	0.3	Iris-setosa
43	4.4	3.2	1.3	0.2	Iris-setosa
44	5.0	3.5	1.6	0.6	Iris-setosa
45	5.1	3.8	1.9	0.4	Iris-setosa
46	4.8	3.0	1.4	0.3	Iris-setosa
47	5.1	3.8	1.6	0.2	Iris-setosa
48	4.6	3.2	1.4	0.2	Iris-setosa
49	5.3	3.7	1.5	0.2	Iris-setosa
50	5.0	3.3	1.4	0.2	Iris-setosa
51	7.0	3.2	4.7	1.4	Iris-versicolor
52	6.4	3.2	4.5	1.5	Iris-versicolor
53	6.9	3.1	4.9	1.5	Iris-versicolor
54	5.5	2.3	4.0	1.3	Iris-versicolor
55	6.5	2.8	4.6	1.5	Iris-versicolor
56	5.7	2.8	4.5	1.3	Iris-versicolor
57	6.3	3.3	4.7	1.6	Iris-versicolor
58	4.9	2.4	3.3	1.0	Iris-versicolor
59	6.6	2.9	4.6	1.3	Iris-versicolor
60	5.2	2.7	3.9	1.4	Iris-versicolor
61	5.0	2.0	3.5	1.0	Iris-versicolor
62	5.9	3.0	4.2	1.5	Iris-versicolor
63	6.0	2.2	4.0	1.0	Iris-versicolor
64	6.1	2.9	4.7	1.4	Iris-versicolor
65	5.6	2.9	3.6	1.3	Iris-versicolor
66	6.7	3.1	4.4	1.4	Iris-versicolor
67	5.6	3.0	4.5	1.5	Iris-versicolor
68	5.8	2.7	4.1	1.0	Iris-versicolor
69	6.2	2.2	4.5	1.5	Iris-versicolor
70	5.6	2.5	3.9	1.1	Iris-versicolor
71	5.9	3.2	4.8	1.8	Iris-versicolor
72	6.1	2.8	4.0	1.3	Iris-versicolor
73	6.3	2.5	4.9	1.5	Iris-versicolor
74	6.1	2.8	4.7	1.2	Iris-versicolor
75	6.4	2.9	4.3	1.3	Iris-versicolor
76	6.6	3.0	4.4	1.4	Iris-versicolor
77	6.8	2.8	4.8	1.4	Iris-versicolor
78	6.7	3.0	5.0	1.7	Iris-versicolor
79	6.0	2.9	4.5	1.5	Iris-versicolor
80	5.7	2.6	3.5	1.0	Iris-versicolor
81	5.5	2.4	3.8	1.1	Iris-versicolor
82	5.5	2.4	3.7	1.0	Iris-versicolor
83	5.8	2.7	3.9	1.2	Iris-versicolor
84	6.0	2.7	5.1	1.6	Iris-versicolor
85	5.4	3.0	4.5	1.5	Iris-versicolor
86	6.0	3.4	4.5	1.6	Iris-versicolor
87	6.7	3.1	4.7	1.5	Iris-versicolor
88	6.3	2.3	4.4	1.3	Iris-versicolor
89	5.6	3.0	4.1	1.3	Iris-versicolor
90	5.5	2.5	4.0	1.3	Iris-versicolor
91	5.5	2.6	4.4	1.2	Iris-versicolor
92	6.1	3.0	4.6	1.4	Iris-versicolor
93	5.8	2.6	4.0	1.2	Iris-versicolor
94	5.0	2.3	3.3	1.0	Iris-versicolor
95	5.6	2.7	4.2	1.3	Iris-versicolor
96	5.7	3.0	4.2	1.2	Iris-versicolor
97	5.7	2.9	4.2	1.3	Iris-versicolor
98	6.2	2.9	4.3	1.3	Iris-versicolor
99	5.1	2.5	3.0	1.1	Iris-versicolor
100	5.7	2.8	4.1	1.3	Iris-versicolor

Rows: 1-100 | Columns: 5

Let’s import the model:

from vastorbit.machine_learning.vast 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(test, name = "prediction"

	123 sepallengthcm Decimal(5, 2)	123 sepalwidthcm Decimal(5, 2)	123 petallengthcm Decimal(5, 2)	123 petalwidthcm Decimal(5, 2)	Abc species Varchar(30)	123 seedrand Decimal(26, 6)	123 prediction_irissetosa Double	123 prediction_irisversicolor Double	123 prediction_irisvirginica Double
1	4.6	3.4	1.4	0.3	Iris-setosa	0.18	0.8056052441762281	0.11580953107351831	0.07858522475025351
2	5.0	3.4	1.5	0.2	Iris-setosa	0.15	0.8107061972685313	0.11371206960745887	0.07558173312400986
3	5.8	4.0	1.2	0.2	Iris-setosa	0.19	0.62343029479827	0.22141913857772202	0.15515056662400806
4	5.7	4.4	1.5	0.4	Iris-setosa	0.16	0.6029761451431291	0.23296067392955477	0.16406318092731592
5	5.1	3.7	1.5	0.4	Iris-setosa	0.06	0.8147916053352589	0.11088458681770906	0.07432380784703212
6	5.2	3.5	1.5	0.2	Iris-setosa	0.19	0.778411849823205	0.13311611637225265	0.08847203380454234
7	4.9	3.1	1.5	0.1	Iris-setosa	0.04	0.757366928638327	0.1460133816645396	0.09661968969713357
8	5.5	3.5	1.3	0.2	Iris-setosa	0.1	0.7044317414373173	0.17648854091142735	0.11907971765125538
9	5.1	3.4	1.5	0.2	Iris-setosa	0.17	0.7971931563495174	0.12196448849319812	0.0808423551572845
10	7.0	3.2	4.7	1.4	Iris-versicolor	0.08	0.1442284633484264	0.4444295688421644	0.4113419678094092
11	5.5	2.3	4.0	1.3	Iris-versicolor	0.17	0.1599882296338519	0.6380026337967301	0.20200913656941794
12	5.8	2.7	3.9	1.2	Iris-versicolor	0.17	0.11846988051830289	0.7348850973189357	0.14664502216276148
13	5.6	2.7	4.2	1.3	Iris-versicolor	0.11	0.09366866249252152	0.7657392681020926	0.14059206940538585
14	5.7	2.9	4.2	1.3	Iris-versicolor	0.0	0.07833971225532778	0.800560929462616	0.12109935828205616
15	6.2	2.9	4.3	1.3	Iris-versicolor	0.16	0.07632614224865482	0.7765978090454448	0.1470760487059003
16	6.3	3.3	6.0	2.5	Iris-virginica	0.21	0.08987672139079464	0.20339833536282656	0.7067249432463788
17	7.1	3.0	5.9	2.1	Iris-virginica	0.17	0.09711382826502442	0.22618263913681386	0.6767035325981617
18	6.5	3.0	5.8	2.2	Iris-virginica	0.05	0.039647361293481474	0.10089659960796148	0.859456039098557
19	6.4	3.2	5.3	2.3	Iris-virginica	0.11	0.08211866410555073	0.22765102455942887	0.6902303113350203
20	6.5	3.0	5.5	1.8	Iris-virginica	0.14	0.05664966656093116	0.1699065169599729	0.773443816479096
21	7.7	3.8	6.7	2.2	Iris-virginica	0.15	0.16300831004564484	0.29705774806162	0.5399339418927352
22	7.7	2.8	6.7	2.0	Iris-virginica	0.11	0.15232551709588077	0.29552544207966464	0.5521490408244546
23	6.2	2.8	4.8	1.8	Iris-virginica	0.09	0.10445349558629033	0.5047129487158764	0.39083355569783323
24	7.9	3.8	6.4	2.0	Iris-virginica	0.18	0.1656983363037234	0.30522046096502803	0.5290812027312486
25	6.4	2.8	5.6	2.2	Iris-virginica	0.03	0.053214470741399095	0.14638266112682077	0.8004028681317801
26	6.3	2.8	5.1	1.5	Iris-virginica	0.11	0.10190092531565959	0.4274748048616703	0.47062426982267025
27	6.1	2.6	5.6	1.4	Iris-virginica	0.08	0.10720448583253318	0.3392407090680941	0.5535548050993727
28	6.4	3.1	5.5	1.8	Iris-virginica	0.03	0.056217303754926236	0.16879940946958605	0.7749832867754879
29	6.9	3.1	5.4	2.1	Iris-virginica	0.21	0.08774010444570852	0.2320799920778162	0.6801799034764752

Rows: 1-29 | Columns: 9

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.