verticapy.machine_learning.vertica.svm.LinearSVC.predict_proba#

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 winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5	0	white
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5	0	white
10	4.6	0.52	0.15	2.1	0.054	8.0	65.0	0.9934	3.9	0.56	13.1	4	0	red
11	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white
12	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
13	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
14	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red
15	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white
16	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
17	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7	1	white
18	4.8	0.17	0.28	2.9	0.03	22.0	111.0	0.9902	3.38	0.34	11.3	7	1	white
19	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white
20	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6	0	white
21	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white
22	4.8	0.29	0.23	1.1	0.044	38.0	180.0	0.98924	3.28	0.34	11.9	6	0	white
23	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5	0	white
24	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white
25	4.8	0.65	0.12	1.1	0.013	4.0	10.0	0.99246	3.32	0.36	13.5	4	0	white
26	4.9	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
27	4.9	0.33	0.31	1.2	0.016	39.0	150.0	0.98713	3.33	0.59	14.0	8	1	white
28	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
29	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
30	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
31	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
32	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
33	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white
34	5.0	0.17	0.56	1.5	0.026	24.0	115.0	0.9906	3.48	0.39	10.8	7	1	white
35	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6	0	white
36	5.0	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
37	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white
38	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white
39	5.0	0.24	0.34	1.1	0.034	49.0	158.0	0.98774	3.32	0.32	13.1	7	1	white
40	5.0	0.255	0.22	2.7	0.043	46.0	153.0	0.99238	3.75	0.76	11.3	6	0	white
41	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
42	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
43	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
44	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8	1	white
45	5.0	0.3	0.33	3.7	0.03	54.0	173.0	0.9887	3.36	0.3	13.0	7	1	white
46	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
47	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
48	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
49	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
50	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white
51	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6	0	white
52	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
53	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
54	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red
55	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
56	5.0	0.42	0.24	2.0	0.06	19.0	50.0	0.9917	3.72	0.74	14.0	8	1	red
57	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
58	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
59	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white
60	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white
61	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red
62	5.0	1.02	0.04	1.4	0.045	41.0	85.0	0.9938	3.75	0.48	10.5	4	0	red
63	5.0	1.04	0.24	1.6	0.05	32.0	96.0	0.9934	3.74	0.62	11.5	5	0	red
64	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
65	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
66	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
67	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white
68	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white
69	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white
70	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7	1	white
71	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6	0	white
72	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
73	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
74	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white
75	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5	0	white
76	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6	0	white
77	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
78	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
79	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
80	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white
81	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
82	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
83	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
84	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
85	5.1	0.42	0.0	1.8	0.044	18.0	88.0	0.99157	3.68	0.73	13.6	7	1	red
86	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7	1	white
87	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red
88	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
89	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5	0	white
90	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red
91	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
92	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
93	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6	0	white
94	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5	0	white
95	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white
96	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
97	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
98	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
99	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white
100	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white

Rows: 1-100 | Columns: 14

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

Let’s import the model:

from verticapy.machine_learning.vertica import LogisticRegression

Then we can create the model:

model = LogisticRegression(
    tol = 1e-6,
    max_iter = 100,
    solver = 'Newton',
    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 Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction_0 Float(22)	123 prediction_1 Float(22)
1	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white	0.763552791289846	0.236447208710154
2	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white	0.976378239220592	0.0236217607794081
3	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white	0.822876683483437	0.177123316516563
4	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5	0	white	0.967309322764945	0.0326906772350551
5	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white	0.796143110028266	0.203856889971734
6	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white	0.90235077228171	0.0976492277182895
7	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red	0.956509247475682	0.043490752524318
8	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white	0.869523144280595	0.130476855719405
9	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7	1	white	0.701820378561087	0.298179621438913
10	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white	0.796354125909301	0.203645874090699
11	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red	0.8814639119409	0.1185360880591
12	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6	0	white	0.669790549228419	0.330209450771581
13	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white	0.917910272264722	0.0820897277352779
14	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white	0.792928805673296	0.207071194326704
15	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white	0.628620413002803	0.371379586997197
16	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6	0	white	0.78381397340982	0.21618602659018
17	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	0.811487036256415	0.188512963743585
18	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red	0.840057284845889	0.159942715154111
19	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	0.743977989775177	0.256022010224823
20	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white	0.947683304992583	0.0523166950074168
21	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white	0.584810662001716	0.415189337998284
22	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white	0.823709562169827	0.176290437830173
23	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white	0.651070046229722	0.348929953770278
24	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red	0.955812379375484	0.0441876206245156
25	5.0	1.04	0.24	1.6	0.05	32.0	96.0	0.9934	3.74	0.62	11.5	5	0	red	0.980534693830442	0.019465306169558
26	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white	0.839631599125684	0.160368400874316
27	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white	0.705715424675369	0.294284575324631
28	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7	1	white	0.690935508825706	0.309064491174294
29	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white	0.818080055525171	0.181919944474829
30	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white	0.818080055525171	0.181919944474829
31	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white	0.686208047524799	0.313791952475201
32	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5	0	white	0.745446827210064	0.254553172789936
33	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white	0.695068373417901	0.304931626582099
34	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white	0.802427813516764	0.197572186483236
35	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	0.789345955007092	0.210654044992908
36	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	0.789345955007092	0.210654044992908
37	5.1	0.42	0.0	1.8	0.044	18.0	88.0	0.99157	3.68	0.73	13.6	7	1	red	0.876038349070872	0.123961650929128
38	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7	1	white	0.739674681954413	0.260325318045587
39	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	0.91715720944453	0.0828427905554702
40	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white	0.651494399697412	0.348505600302588
41	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6	0	white	0.811906521906598	0.188093478093402
42	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5	0	white	0.853085882402191	0.14691411759781
43	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white	0.852898171395376	0.147101828604624
44	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white	0.836540707691761	0.163459292308239
45	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white	0.662514335399642	0.337485664600358
46	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white	0.863110699404046	0.136889300595954
47	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white	0.807180460307446	0.192819539692554
48	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6	0	white	0.846638927910848	0.153361072089152
49	5.2	0.285	0.29	5.15	0.035	64.0	138.0	0.9895	3.19	0.34	12.4	8	1	white	0.592778306935162	0.407221693064838
50	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7	1	white	0.620895502687567	0.379104497312433
51	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red	0.85652543122198	0.14347456877802
52	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white	0.728628706947064	0.271371293052936
53	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white	0.93388668800563	0.0661133119943704
54	5.2	0.37	0.33	1.2	0.028	13.0	81.0	0.9902	3.37	0.38	11.7	6	0	white	0.778745141990139	0.221254858009861
55	5.2	0.405	0.15	1.45	0.038	10.0	44.0	0.99125	3.52	0.4	11.6	4	0	white	0.853708405914142	0.146291594085858
56	5.2	0.49	0.26	2.3	0.09	23.0	74.0	0.9953	3.71	0.62	12.2	6	0	red	0.972892474841486	0.027107525158514
57	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white	0.760148271515295	0.239851728484705
58	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white	0.800815120702702	0.199184879297298
59	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	0.843979654588235	0.156020345411765
60	5.3	0.21	0.29	0.7	0.028	11.0	66.0	0.99215	3.3	0.4	9.8	5	0	white	0.854545629342478	0.145454370657522
61	5.3	0.24	0.33	1.3	0.033	25.0	97.0	0.9906	3.59	0.38	11.0	8	1	white	0.756364572214314	0.243635427785686
62	5.3	0.26	0.23	5.15	0.034	48.0	160.0	0.9952	3.82	0.51	10.5	7	1	white	0.931544317338777	0.0684556826612226
63	5.3	0.275	0.24	7.4	0.038	28.0	114.0	0.99313	3.38	0.51	11.0	6	0	white	0.823790906604464	0.176209093395536
64	5.3	0.3	0.16	4.2	0.029	37.0	100.0	0.9905	3.3	0.36	11.8	8	1	white	0.706154690591548	0.293845309408452
65	5.3	0.3	0.2	1.1	0.077	48.0	166.0	0.9944	3.3	0.54	8.7	4	0	white	0.949278008776347	0.0507219912236525
66	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white	0.488825025086502	0.511174974913498
67	5.3	0.32	0.12	6.6	0.043	22.0	141.0	0.9937	3.36	0.6	10.4	6	0	white	0.878186118379567	0.121813881620433
68	5.3	0.32	0.23	9.65	0.026	26.0	119.0	0.99168	3.18	0.53	12.2	6	0	white	0.678913838224292	0.321086161775708
69	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white	0.783485533163016	0.216514466836984
70	5.3	0.395	0.07	1.3	0.035	26.0	102.0	0.992	3.5	0.35	10.6	6	0	white	0.883430457782422	0.116569542217578
71	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white	0.765633445796252	0.234366554203748
72	5.3	0.43	0.11	1.1	0.029	6.0	51.0	0.99076	3.51	0.48	11.2	4	0	white	0.830788409635164	0.169211590364836
73	5.3	0.47	0.1	1.3	0.036	11.0	74.0	0.99082	3.48	0.54	11.2	4	0	white	0.84346343049223	0.15653656950777
74	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red	0.882179180424396	0.117820819575604
75	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white	0.93809864954753	0.0619013504524704
76	5.3	0.585	0.07	7.1	0.044	34.0	145.0	0.9945	3.34	0.57	9.7	6	0	white	0.939770744551846	0.0602292554481537
77	5.4	0.15	0.32	2.5	0.037	10.0	51.0	0.98878	3.04	0.58	12.6	6	0	white	0.512224475669363	0.487775524330637
78	5.4	0.17	0.27	2.7	0.049	28.0	104.0	0.99224	3.46	0.55	10.3	6	0	white	0.818349250188277	0.181650749811723
79	5.4	0.22	0.35	6.5	0.029	26.0	87.0	0.99092	3.29	0.44	12.5	7	1	white	0.638053386161091	0.361946613838909
80	5.4	0.23	0.36	1.5	0.03	74.0	121.0	0.98976	3.24	0.99	12.1	7	1	white	0.666920650315313	0.333079349684687
81	5.4	0.24	0.18	2.3	0.05	22.0	145.0	0.99207	3.24	0.46	10.3	5	0	white	0.835502758965488	0.164497241034512
82	5.4	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	6	0	white	0.753928346065006	0.246071653934994
83	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0.624146061277618	0.375853938722382
84	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0.624146061277618	0.375853938722382
85	5.4	0.29	0.47	3.0	0.052	47.0	145.0	0.993	3.29	0.75	10.0	6	0	white	0.879846459658329	0.120153540341671
86	5.4	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white	0.477717821998554	0.522282178001446
87	5.4	0.31	0.47	3.0	0.053	46.0	144.0	0.9931	3.29	0.76	10.0	5	0	white	0.888098133917156	0.111901866082844
88	5.4	0.375	0.4	3.3	0.054	29.0	147.0	0.99482	3.42	0.52	9.1	5	0	white	0.946284808860598	0.0537151911394022
89	5.4	0.415	0.19	1.6	0.039	27.0	88.0	0.99265	3.54	0.41	10.0	7	1	white	0.904211077671388	0.095788922328612
90	5.4	0.45	0.27	6.4	0.033	20.0	102.0	0.98944	3.22	0.27	13.4	8	1	white	0.604412632476083	0.395587367523917
91	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0.659863656368692	0.340136343631308
92	5.4	0.59	0.07	7.0	0.045	36.0	147.0	0.9944	3.34	0.57	9.7	6	0	white	0.936163982877994	0.0638360171220063
93	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white	0.936033000181533	0.0639669998184667
94	5.4	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red	0.947658031416905	0.0523419685830949
95	5.4	0.835	0.08	1.2	0.046	13.0	93.0	0.9924	3.57	0.85	13.0	7	1	red	0.95274797279126	0.0472520272087398
96	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white	0.783940493145178	0.216059506854822
97	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white	0.90229083559261	0.09770916440739
98	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white	0.90229083559261	0.09770916440739
99	5.5	0.15	0.32	14.0	0.031	16.0	99.0	0.99437	3.26	0.38	11.5	8	1	white	0.717987781990906	0.282012218009094
100	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white	0.637817161298322	0.362182838701678

Rows: 1-100 | Columns: 16

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.