vastorbit.machine_learning.vast.svm.LinearSVC.predict_proba

LinearSVC.predict_proba(vdf: Annotated[str | VastFrame, ''], 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) → VastFrame

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 VastColumn. 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 VastFrame.

Returns:

the input object.

Return type:

VastFrame

Examples

For this example, we will use the winequality dataset.

import vastorbit.datasets as vod

data = vod.load_winequality()

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white
2	7.4	0.4	0.29	5.4	0.044	31.0	122.0	0.994	3.3	0.5	11.1	8	1	white
3	7.1	0.26	0.31	2.2	0.044	29.0	128.0	0.9937	3.34	0.64	10.9	8	1	white
4	9.0	0.31	0.48	6.6	0.043	11.0	73.0	0.9938	2.9	0.38	11.6	5	0	white
5	6.3	0.39	0.24	6.9	0.069	9.0	117.0	0.9942	3.15	0.35	10.2	4	0	white
6	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white
7	7.1	0.19	0.28	3.6	0.033	16.0	78.0	0.993	2.91	0.78	11.4	6	0	white
8	7.3	0.25	0.36	13.1	0.05	35.0	200.0	0.9986	3.04	0.46	8.9	7	1	white
9	7.9	0.2	0.34	1.2	0.04	29.0	118.0	0.9932	3.14	0.41	10.6	6	0	white
10	7.1	0.26	0.32	5.9	0.037	39.0	97.0	0.9934	3.31	0.4	11.6	6	0	white
11	7.0	0.2	0.34	5.7	0.035	32.0	83.0	0.9928	3.19	0.46	11.5	6	0	white
12	6.9	0.3	0.33	4.1	0.035	26.0	155.0	0.9925	3.25	0.79	12.3	8	1	white
13	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
14	5.8	0.17	0.3	1.4	0.037	55.0	130.0	0.9909	3.29	0.38	11.3	6	0	white
15	5.9	0.415	0.02	0.8	0.038	22.0	63.0	0.9932	3.36	0.36	9.3	5	0	white
16	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white
17	8.6	0.55	0.35	15.55	0.057	35.5	366.5	1.0001	3.04	0.63	11.0	3	0	white
18	6.9	0.35	0.74	1.0	0.044	18.0	132.0	0.992	3.13	0.55	10.2	5	0	white
19	7.6	0.14	0.74	1.6	0.04	27.0	103.0	0.9916	3.07	0.4	10.8	7	1	white
20	9.2	0.28	0.49	11.8	0.042	29.0	137.0	0.998	3.1	0.34	10.1	4	0	white
21	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white
22	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
23	9.8	0.25	0.74	10.0	0.056	36.0	225.0	0.9977	3.06	0.43	10.0	4	0	white
24	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
25	6.8	0.22	0.49	0.9	0.052	26.0	128.0	0.991	3.25	0.35	11.4	6	0	white
26	7.2	0.22	0.49	1.0	0.045	34.0	140.0	0.99	3.05	0.34	12.7	6	0	white
27	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
28	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
29	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
30	7.0	0.39	0.24	1.0	0.048	8.0	119.0	0.9923	3.0	0.31	10.1	4	0	white
31	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white
32	6.5	0.36	0.49	2.9	0.03	16.0	94.0	0.9902	3.1	0.49	12.1	7	1	white
33	7.1	0.29	0.49	1.2	0.031	32.0	99.0	0.9893	3.07	0.33	12.2	6	0	white
34	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
35	6.9	0.23	0.24	14.2	0.053	19.0	94.0	0.9982	3.17	0.5	9.6	5	0	white
36	8.5	0.56	0.74	17.85	0.051	51.0	243.0	1.0005	2.99	0.7	9.2	5	0	white
37	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
38	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white
39	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white
40	7.2	0.26	0.74	13.6	0.05	56.0	162.0	0.998	3.03	0.44	8.8	5	0	white
41	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white
42	8.0	0.25	0.49	1.2	0.061	27.0	117.0	0.9938	3.08	0.34	9.4	5	0	white
43	7.0	0.18	0.49	5.3	0.04	34.0	125.0	0.9914	3.24	0.4	12.2	6	0	white
44	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
45	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
46	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
47	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
48	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
49	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
50	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
51	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
52	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
53	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
54	8.3	0.21	0.49	19.8	0.054	50.0	231.0	1.0012	2.99	0.54	9.2	5	0	white
55	6.9	0.34	0.74	11.2	0.069	44.0	150.0	0.9968	3.0	0.81	9.2	5	0	white
56	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
57	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
58	7.1	0.22	0.74	2.7	0.044	42.0	144.0	0.991	3.31	0.41	12.2	6	0	white
59	7.9	0.11	0.49	4.5	0.048	27.0	133.0	0.9946	3.24	0.42	10.6	6	0	white
60	8.5	0.17	0.74	3.6	0.05	29.0	128.0	0.9928	3.28	0.4	12.4	6	0	white
61	6.4	0.145	0.49	5.4	0.048	54.0	164.0	0.9946	3.56	0.44	10.8	6	0	white
62	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
63	8.3	0.19	0.49	1.2	0.051	11.0	137.0	0.9918	3.06	0.46	11.0	6	0	white
64	8.0	0.44	0.49	9.1	0.031	46.0	151.0	0.9926	3.16	0.27	12.7	8	1	white
65	7.0	0.2	0.74	0.8	0.044	19.0	163.0	0.9931	3.46	0.53	10.2	5	0	white
66	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
67	7.1	0.25	0.49	3.0	0.03	30.0	96.0	0.9903	3.13	0.39	12.3	7	1	white
68	6.5	0.24	0.24	1.6	0.046	15.0	60.0	0.9928	3.19	0.39	9.8	5	0	white
69	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
70	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
71	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
72	7.8	0.21	0.49	1.35	0.052	6.0	48.0	0.9911	3.15	0.28	11.4	5	0	white
73	7.0	0.2	0.49	5.9	0.038	39.0	128.0	0.9938	3.21	0.48	10.8	6	0	white
74	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
75	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
76	7.1	0.85	0.49	8.7	0.028	40.0	184.0	0.9962	3.22	0.36	10.7	5	0	white
77	7.6	0.51	0.24	1.2	0.04	10.0	104.0	0.992	3.05	0.29	10.8	6	0	white
78	7.9	0.22	0.24	4.6	0.044	39.0	159.0	0.9927	2.99	0.28	11.5	6	0	white
79	7.7	0.16	0.49	2.0	0.056	20.0	124.0	0.9948	3.32	0.49	10.7	6	0	white
80	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
81	6.6	0.25	0.24	1.7	0.048	26.0	124.0	0.9942	3.37	0.6	10.1	6	0	white
82	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white
83	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
84	7.5	0.32	0.24	4.6	0.053	8.0	134.0	0.9958	3.14	0.5	9.1	3	0	white
85	7.4	0.28	0.49	1.5	0.034	20.0	126.0	0.9918	2.98	0.39	10.6	6	0	white
86	6.2	0.15	0.49	0.9	0.033	17.0	51.0	0.9932	3.3	0.7	9.4	6	0	white
87	6.7	0.25	0.74	19.4	0.054	44.0	169.0	1.0004	3.51	0.45	9.8	6	0	white
88	6.5	0.26	0.74	13.3	0.044	68.0	224.0	0.9972	3.18	0.54	9.5	6	0	white
89	7.9	0.16	0.74	17.85	0.037	52.0	187.0	0.9998	2.99	0.41	9.3	5	0	white
90	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white
91	7.5	0.2	0.49	1.3	0.031	8.0	97.0	0.9918	3.06	0.62	11.1	5	0	white
92	8.0	0.3	0.49	9.4	0.046	47.0	188.0	0.9964	3.14	0.48	10.0	5	0	white
93	8.0	0.34	0.49	9.0	0.033	39.0	180.0	0.9936	3.13	0.38	12.3	8	1	white
94	7.7	0.35	0.49	8.65	0.033	42.0	186.0	0.9931	3.14	0.38	12.4	8	1	white
95	7.6	0.29	0.49	9.6	0.03	45.0	197.0	0.9938	3.13	0.38	12.3	7	1	white
96	6.7	0.62	0.24	1.1	0.039	6.0	62.0	0.9934	3.41	0.32	10.4	5	0	white
97	6.8	0.27	0.49	1.2	0.044	35.0	126.0	0.99	3.13	0.48	12.1	7	1	white
98	7.7	0.27	0.49	1.8	0.041	23.0	86.0	0.9914	3.16	0.42	12.5	6	0	white
99	6.7	0.51	0.24	2.1	0.043	14.0	155.0	0.9904	3.22	0.6	13.0	6	0	white
100	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white

Rows: 1-100 | Columns: 14

train, test = data.train_test_split(test_size = 0.5)

Let’s import the model:

from vastorbit.machine_learning.vast import LogisticRegression

Then we can create the model:

model = LogisticRegression(
    tol = 1e-6,
    max_iter = 100,
    solver = 'lbfgs',
    fit_intercept = True,
)

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "good",
    test,
)

model.predict_proba(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "prediction",
)

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 seedrand Decimal(26, 6)	123 prediction_0 Double	123 prediction_1 Double
1	9.9	0.35	0.38	1.5	0.0579999999999999	31.0	47.0	0.99676	3.26	0.82	10.6	7	1	red	0.14	0.8022098300306251	0.19779016996937496
2	9.1	0.29	0.33	2.05	0.063	13.0	27.0	0.99516	3.26	0.84	11.7	7	1	red	0.06	0.7862877026992167	0.21371229730078334
3	6.8	0.36	0.32	1.8	0.067	4.0	8.0	0.9928	3.36	0.55	12.8	7	1	red	0.26	0.7992590046085079	0.20074099539149215
4	8.2	0.43	0.29	1.6	0.081	27.0	45.0	0.99603	3.25	0.54	10.3	5	0	red	0.23	0.8388490843576185	0.16115091564238151
5	6.8	0.36	0.32	1.8	0.067	4.0	8.0	0.9928	3.36	0.55	12.8	7	1	red	0.49	0.7992590046085079	0.20074099539149215
6	8.9	0.35	0.4	3.6	0.11	12.0	24.0	0.99549	3.23	0.7	12.0	7	1	red	0.15	0.8415713404509542	0.1584286595490459
7	8.9	0.28	0.45	1.7	0.067	7.0	12.0	0.99354	3.25	0.55	12.3	7	1	red	0.04	0.7703358717555047	0.2296641282444953
8	10.5	0.39	0.46	2.2	0.075	14.0	27.0	0.99598	3.06	0.84	11.4	6	0	red	0.42	0.8300480987110808	0.1699519012889193
9	8.0	0.18	0.37	0.9	0.049	36.0	109.0	0.99007	2.89	0.44	12.7	6	0	red	0.46	0.7025926164076676	0.29740738359233243
10	7.0	0.5	0.14	1.8	0.078	10.0	23.0	0.99636	3.53	0.61	10.4	5	0	red	0.44	0.8625558322604436	0.1374441677395563
11	11.3	0.36	0.66	2.4	0.123	3.0	8.0	0.99642	3.2	0.53	11.9	6	0	red	0.31	0.8374991394329018	0.16250086056709825
12	8.6	0.83	0.0	2.8	0.095	17.0	43.0	0.99822	3.33	0.6	10.4	6	0	red	0.32	0.9457100935719827	0.05428990642801732
13	7.3	0.55	0.01	1.8	0.093	9.0	15.0	0.99514	3.35	0.58	11.0	7	1	red	0.38	0.8882919619306597	0.1117080380693403
14	8.1	0.82	0.0	4.1	0.095	5.0	14.0	0.99854	3.36	0.53	9.6	5	0	red	0.02	0.947803380801488	0.052196619198512074
15	7.5	0.57	0.08	2.6	0.089	14.0	27.0	0.99592	3.3	0.59	10.4	6	0	red	0.26	0.8943089171329011	0.10569108286709888
16	8.9	0.745	0.18	2.5	0.077	15.0	48.0	0.99739	3.2	0.47	9.7	6	0	red	0.18	0.9247338684944846	0.07526613150551544
17	10.1	0.37	0.34	2.4	0.085	5.0	17.0	0.99683	3.17	0.65	10.6	7	1	red	0.2	0.8353814045235841	0.16461859547641583
18	7.3	0.91	0.1	1.8	0.074	20.0	56.0	0.99672	3.35	0.56	9.2	5	0	red	0.0	0.9434747347051694	0.0565252652948306
19	8.7	0.41	0.41	6.2	0.078	25.0	42.0	0.9953	3.24	0.77	12.6	7	1	red	0.45	0.8646394674962502	0.13536053250374983
20	8.9	0.5	0.21	2.2	0.088	21.0	39.0	0.99692	3.33	0.83	11.1	6	0	red	0.46	0.874205844587108	0.12579415541289193
21	7.4	0.965	0.0	2.2	0.088	16.0	32.0	0.99756	3.58	0.67	10.2	5	0	red	0.15	0.9554824317004693	0.04451756829953066
22	6.9	0.49	0.19	1.7	0.079	13.0	26.0	0.99547	3.38	0.64	9.8	6	0	red	0.09	0.8562274257334891	0.1437725742665109
23	8.9	0.5	0.21	2.2	0.088	21.0	39.0	0.99692	3.33	0.83	11.1	6	0	red	0.04	0.874205844587108	0.12579415541289193
24	6.9	0.44	0.0	1.4	0.07	32.0	38.0	0.99438	3.32	0.58	11.4	6	0	red	0.06	0.845235491932733	0.154764508067267
25	7.1	0.43	0.17	1.8	0.0819999999999999	27.0	51.0	0.99634	3.49	0.64	10.4	5	0	red	0.44	0.8424973065565682	0.15750269344343185
26	9.3	0.49	0.36	1.7	0.081	3.0	14.0	0.99702	3.27	0.78	10.9	6	0	red	0.27	0.8591998617447592	0.14080013825524085
27	7.1	0.43	0.17	1.8	0.0819999999999999	27.0	51.0	0.99634	3.49	0.64	10.4	5	0	red	0.05	0.8424973065565682	0.15750269344343185
28	8.5	0.46	0.59	1.4	0.414	16.0	45.0	0.99702	3.03	1.34	9.2	5	0	red	0.39	0.940077277958411	0.05992272204158895
29	5.6	0.605	0.05	2.4	0.073	19.0	25.0	0.99258	3.56	0.55	12.9	5	0	red	0.17	0.8897976196879619	0.1102023803120381
30	7.6	0.42	0.25	3.9	0.104	28.0	90.0	0.99784	3.15	0.57	9.1	5	0	red	0.17	0.8641708215645311	0.1358291784354689
31	9.9	0.53	0.57	2.4	0.093	30.0	52.0	0.9971	3.19	0.76	11.6	7	1	red	0.37	0.8722728387608021	0.12772716123919797
32	8.9	0.48	0.53	4.0	0.1009999999999999	3.0	10.0	0.99586	3.21	0.59	12.1	7	1	red	0.02	0.871425029532698	0.1285749704673021
33	11.6	0.23	0.57	1.8	0.074	3.0	8.0	0.9981	3.14	0.7	9.9	6	0	red	0.41	0.7641762186874025	0.2358237813125975
34	8.0	0.38	0.44	1.9	0.098	6.0	15.0	0.9956	3.3	0.64	11.4	6	0	red	0.17	0.8231985862592898	0.17680141374071023
35	10.2	0.29	0.65	2.4	0.075	6.0	17.0	0.99565	3.22	0.63	11.8	6	0	red	0.43	0.7807615307949888	0.21923846920501125
36	8.2	0.74	0.09	2.0	0.067	5.0	10.0	0.99418	3.28	0.57	11.8	6	0	red	0.26	0.9198569054373986	0.08014309456260135
37	7.7	0.61	0.18	2.4	0.083	6.0	20.0	0.9963	3.29	0.6	10.2	6	0	red	0.06	0.896610038458744	0.10338996154125604
38	8.0	0.62	0.35	2.8	0.086	28.0	52.0	0.997	3.31	0.62	10.8	5	0	red	0.1	0.8964015718162233	0.10359842818377671
39	9.3	0.33	0.45	1.5	0.057	19.0	37.0	0.99498	3.18	0.89	11.1	7	1	red	0.24	0.7851216922611489	0.21487830773885105
40	7.5	0.77	0.2	8.1	0.098	30.0	92.0	0.99892	3.2	0.58	9.2	5	0	red	0.36	0.9498098999025067	0.050190100097493265
41	7.5	0.77	0.2	8.1	0.098	30.0	92.0	0.99892	3.2	0.58	9.2	5	0	red	0.01	0.9498098999025067	0.050190100097493265
42	9.9	0.32	0.56	2.0	0.073	3.0	8.0	0.99534	3.15	0.73	11.4	6	0	red	0.21	0.7917365693734749	0.2082634306265251
43	8.6	0.37	0.65	6.4	0.08	3.0	8.0	0.99817	3.27	0.58	11.0	5	0	red	0.08	0.8421434484853685	0.15785655151463146
44	8.6	0.37	0.65	6.4	0.08	3.0	8.0	0.99817	3.27	0.58	11.0	5	0	red	0.07	0.8421434484853685	0.15785655151463146
45	7.9	0.3	0.68	8.3	0.05	37.5	278.0	0.99316	3.01	0.51	12.3	7	1	red	0.42	0.8153036104814738	0.18469638951852618
46	10.3	0.27	0.56	1.4	0.047	3.0	8.0	0.99471	3.16	0.51	11.8	6	0	red	0.08	0.7508459738064129	0.2491540261935871
47	7.9	0.3	0.68	8.3	0.05	37.5	289.0	0.99316	3.01	0.51	12.3	7	1	red	0.15	0.8153036104814738	0.18469638951852618
48	7.2	0.38	0.3	1.8	0.073	31.0	70.0	0.99685	3.42	0.59	9.5	6	0	red	0.13	0.8139293633135875	0.18607063668641255
49	8.7	0.42	0.45	2.4	0.072	32.0	59.0	0.99617	3.33	0.77	12.0	6	0	red	0.16	0.8314215686554134	0.16857843134458655
50	7.2	0.38	0.3	1.8	0.073	31.0	70.0	0.99685	3.42	0.59	9.5	6	0	red	0.24	0.8139293633135875	0.18607063668641255
51	6.8	0.48	0.08	1.8	0.074	40.0	64.0	0.99529	3.12	0.49	9.6	5	0	red	0.32	0.8573615159606451	0.14263848403935486
52	8.5	0.34	0.4	4.7	0.055	3.0	9.0	0.99738	3.38	0.66	11.6	7	1	red	0.14	0.8192299902058852	0.18077000979411484
53	7.9	0.19	0.42	1.6	0.057	18.0	30.0	0.994	3.29	0.69	11.2	6	0	red	0.17	0.717704764322928	0.2822952356770721
54	7.9	0.34	0.42	2.0	0.086	8.0	19.0	0.99546	3.35	0.6	11.4	6	0	red	0.33	0.8043180618794817	0.19568193812051826
55	7.0	0.54	0.09	2.0	0.081	10.0	16.0	0.99479	3.43	0.59	11.5	6	0	red	0.05	0.8781175702556165	0.12188242974438349
56	9.2	0.31	0.36	2.2	0.079	11.0	31.0	0.99615	3.33	0.86	12.0	7	1	red	0.43	0.8032291941109859	0.19677080588901402
57	9.4	0.4	0.47	2.5	0.087	6.0	20.0	0.99772	3.15	0.5	10.5	5	0	red	0.49	0.8356389536911883	0.16436104630881168
58	8.6	0.52	0.38	1.5	0.096	5.0	18.0	0.99666	3.2	0.52	9.4	5	0	red	0.34	0.8680194350524204	0.13198056494757965
59	8.4	0.34	0.42	2.1	0.072	23.0	36.0	0.99392	3.11	0.78	12.4	6	0	red	0.11	0.8004079092917775	0.19959209070822248
60	7.4	0.49	0.27	2.1	0.071	14.0	25.0	0.99388	3.35	0.63	12.0	6	0	red	0.17	0.8538627404758804	0.14613725952411957
61	7.4	0.49	0.27	2.1	0.071	14.0	25.0	0.99388	3.35	0.63	12.0	6	0	red	0.2	0.8538627404758804	0.14613725952411957
62	8.3	0.53	0.0	1.4	0.07	6.0	14.0	0.99593	3.25	0.64	10.0	6	0	red	0.34	0.876820073919668	0.12317992608033197
63	8.9	0.24	0.39	1.6	0.074	3.0	10.0	0.99698	3.12	0.59	9.5	6	0	red	0.18	0.7617529738822331	0.23824702611776688
64	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red	0.29	0.80470467680739	0.19529532319261003
65	7.0	0.69	0.07	2.5	0.091	15.0	21.0	0.99572	3.38	0.6	11.3	6	0	red	0.06	0.917093865763145	0.08290613423685499
66	7.0	0.69	0.07	2.5	0.091	15.0	21.0	0.99572	3.38	0.6	11.3	6	0	red	0.06	0.917093865763145	0.08290613423685499
67	7.0	0.69	0.07	2.5	0.091	15.0	21.0	0.99572	3.38	0.6	11.3	6	0	red	0.19	0.917093865763145	0.08290613423685499
68	5.6	0.66	0.0	2.5	0.066	7.0	15.0	0.99256	3.52	0.58	12.9	5	0	red	0.25	0.9021656344692397	0.09783436553076034
69	6.6	0.5	0.0	1.8	0.062	21.0	28.0	0.99352	3.44	0.55	12.3	6	0	red	0.31	0.8611270606149245	0.1388729393850755
70	6.5	0.58	0.0	2.2	0.096	3.0	13.0	0.99557	3.62	0.62	11.5	4	0	red	0.42	0.8961051802408134	0.10389481975918662
71	5.8	0.29	0.26	1.7	0.063	3.0	11.0	0.9915	3.39	0.54	13.5	6	0	red	0.26	0.7657484715514973	0.23425152844850278
72	10.5	0.43	0.35	3.3	0.092	24.0	70.0	0.99798	3.21	0.69	10.5	6	0	red	0.23	0.8651745038356048	0.13482549616439518
73	5.9	0.19	0.21	1.7	0.045	57.0	135.0	0.99341	3.32	0.44	9.5	5	0	red	0.06	0.7136439482556116	0.2863560517443884
74	7.2	0.48	0.07	5.5	0.089	10.0	18.0	0.99684	3.37	0.68	11.2	7	1	red	0.23	0.8913083263934418	0.1086916736065582
75	8.5	0.28	0.35	1.7	0.061	6.0	15.0	0.99524	3.3	0.74	11.8	7	1	red	0.43	0.7718116086850922	0.22818839131490778
76	10.4	0.52	0.45	2.0	0.08	6.0	13.0	0.99774	3.22	0.76	11.4	6	0	red	0.49	0.869628859461336	0.130371140538664
77	10.4	0.52	0.45	2.0	0.08	6.0	13.0	0.99774	3.22	0.76	11.4	6	0	red	0.44	0.869628859461336	0.130371140538664
78	7.0	0.22	0.3	1.8	0.065	16.0	20.0	0.99672	3.61	0.82	10.0	6	0	red	0.4	0.7435028797465597	0.25649712025344024
79	7.3	0.32	0.23	2.3	0.066	35.0	70.0	0.99588	3.43	0.62	10.1	5	0	red	0.36	0.7983385646359079	0.20166143536409206
80	10.0	0.41	0.45	6.2	0.071	6.0	14.0	0.99702	3.21	0.49	11.8	7	1	red	0.17	0.8655771877394853	0.13442281226051467
81	10.0	0.35	0.47	2.0	0.061	6.0	11.0	0.99585	3.23	0.52	12.0	6	0	red	0.47	0.8032959846895329	0.1967040153104671
82	8.2	0.33	0.32	2.8	0.067	4.0	12.0	0.99473	3.3	0.76	12.8	7	1	red	0.43	0.8067744993968207	0.19322550060317936
83	8.3	0.6	0.25	2.2	0.118	9.0	38.0	0.99616	3.15	0.53	9.8	5	0	red	0.06	0.9036256506347036	0.09637434936529649
84	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red	0.08	0.8405941227419684	0.15940587725803151
85	8.5	0.32	0.42	2.3	0.075	12.0	19.0	0.99434	3.14	0.71	11.8	7	1	red	0.45	0.7972512850110723	0.2027487149889277
86	9.0	0.785	0.24	1.7	0.078	10.0	21.0	0.99692	3.29	0.67	10.0	5	0	red	0.07	0.9263545650098175	0.07364543499018252
87	9.9	0.54	0.26	2.0	0.111	7.0	60.0	0.99709	2.94	0.98	10.2	5	0	red	0.45	0.8924622208801851	0.10753777911981484
88	9.2	0.36	0.34	1.6	0.062	5.0	12.0	0.99667	3.2	0.67	10.5	6	0	red	0.48	0.8076902542130153	0.19230974578698462
89	9.7	0.42	0.46	2.1	0.074	5.0	16.0	0.99649	3.27	0.74	12.3	6	0	red	0.27	0.8343562282678675	0.1656437717321325
90	6.5	0.61	0.0	2.2	0.095	48.0	59.0	0.99541	3.61	0.7	11.5	6	0	red	0.31	0.902170625639622	0.09782937436037796
91	7.1	0.66	0.0	2.4	0.052	6.0	11.0	0.99318	3.35	0.66	12.7	7	1	red	0.36	0.9022811724864364	0.0977188275135637
92	8.2	0.35	0.33	2.4	0.076	11.0	47.0	0.99599	3.27	0.81	11.0	6	0	red	0.24	0.8143213031680618	0.18567869683193816
93	9.8	0.39	0.43	1.65	0.068	5.0	11.0	0.99478	3.19	0.46	11.4	5	0	red	0.0	0.8193780255219937	0.18062197447800635
94	10.2	0.4	0.4	2.5	0.068	41.0	54.0	0.99754	3.38	0.86	10.5	6	0	red	0.47	0.8347904848505509	0.16520951514944912
95	6.7	0.64	0.23	2.1	0.08	11.0	119.0	0.99538	3.36	0.7	10.9	5	0	red	0.28	0.8948333739239671	0.10516662607603285
96	7.0	0.43	0.3	2.0	0.085	6.0	39.0	0.99346	3.33	0.46	11.9	6	0	red	0.46	0.8373275746974713	0.1626724253025287
97	9.1	0.4	0.57	4.6	0.08	6.0	20.0	0.99652	3.28	0.57	12.5	6	0	red	0.16	0.8430924382775016	0.15690756172249842
98	6.4	0.885	0.0	2.3	0.166	6.0	12.0	0.99551	3.56	0.51	10.8	5	0	red	0.08	0.9576532703971975	0.042346729602802516
99	8.6	0.42	0.39	1.8	0.068	6.0	12.0	0.99516	3.35	0.69	11.7	8	1	red	0.3	0.8270408963542331	0.17295910364576697
100	7.2	0.36	0.46	2.1	0.074	24.0	44.0	0.99534	3.4	0.85	11.0	7	1	red	0.07	0.7992044375316747	0.2007955624683253

Rows: 1-100 | Columns: 17

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.