Feedback

×

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

verticapy.machine_learning.vertica.linear_model.ElasticNet.predict

ElasticNet.predict(vdf: str | vDataFrame, X: str | list[str] | None = None, name: str | None = None, inplace: bool = True) → vDataFrame

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 :py:class`vDataColumn`. If empty, a name is generated.

inplace: bool, optional

If set to True, the prediction is added to the :py:class`vDataFrame`.

Returns

vDataFrame

the input object.

Examples

We import verticapy:

import verticapy as vp

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

Divide your dataset into training and testing subsets.

data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Let’s import the model:

from verticapy.machine_learning.vertica import LinearRegression

Then we can create the model:

model = LinearRegression(
    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",
    ],
    "quality",
    test,
)

Prediction is straight-forward:

model.predict(
    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 Float(22)
1	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	5.67871806684332
2	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	6.00231021980031
3	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	5.70762499831349
4	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	5.76202260540339
5	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	5.78576069656216
6	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	5.98683432288837
7	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	6.0066392749257
8	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	5.98691637991504
9	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	5.79242350191234
10	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	6.32714684433427
11	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	4.98299789348783
12	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	5.00196938196768
13	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	6.185475344688
14	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	6.08791719306166
15	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	5.51497388926404
16	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	5.82903381959292
17	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	5.88125256736205
18	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	5.89826121745398
19	5.2	0.335	0.2	1.7	0.033	17.0	74.0	0.99002	3.34	0.48	12.3	6	0	white	6.06315230108402
20	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	5.96743433724723
21	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	6.08709644490798
22	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	5.19780041936809
23	5.2	0.6	0.07	7.0	0.044	33.0	147.0	0.9944	3.33	0.58	9.7	5	0	white	5.48117667670451
24	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	6.02630847819319
25	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	5.52867539146632
26	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	5.44320646033304
27	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	5.7825197916722
28	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	6.19044161000019
29	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white	5.98100799482566
30	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	5.4968016921986
31	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white	5.88707035750085
32	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	6.17390224215714
33	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	6.04277130702741
34	5.4	0.265	0.28	7.8	0.052	27.0	91.0	0.99432	3.19	0.38	10.4	6	0	white	5.77484675430745
35	5.4	0.27	0.22	4.6	0.022	29.0	107.0	0.98889	3.33	0.54	13.8	6	0	white	6.42818648545781
36	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	6.45723437619378
37	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	5.64768061341346
38	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red	5.22555084345859
39	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	5.36802694126538
40	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	5.98180121842989
41	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	5.66088099325535
42	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	6.13270803741301
43	5.5	0.16	0.22	4.5	0.03	30.0	102.0	0.9938	3.24	0.36	9.4	6	0	white	5.80918691780289
44	5.5	0.21	0.25	1.2	0.04	18.0	75.0	0.99006	3.31	0.56	11.3	6	0	white	6.16500027909001
45	5.5	0.3	0.25	1.9	0.029	33.0	118.0	0.98972	3.36	0.66	12.5	6	0	white	6.17688412394179
46	5.5	0.485	0.0	1.5	0.065	8.0	103.0	0.994	3.63	0.4	9.7	4	0	white	5.44075359979306
47	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	6.13765608753448
48	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	6.14913737792301
49	5.6	0.19	0.39	1.1	0.043	17.0	67.0	0.9918	3.23	0.53	10.3	6	0	white	5.91489800789594
50	5.6	0.21	0.4	1.3	0.041	81.0	147.0	0.9901	3.22	0.95	11.6	8	1	white	6.15344960581317
51	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	6.1506416243715
52	5.6	0.25	0.26	3.6	0.037	18.0	115.0	0.9904	3.42	0.5	12.6	6	0	white	6.20315532968201
53	5.6	0.255	0.57	10.7	0.056	66.0	171.0	0.99464	3.25	0.61	10.4	7	1	white	5.84410052436729
54	5.6	0.26	0.0	10.2	0.038	13.0	111.0	0.99315	3.44	0.46	12.4	6	0	white	6.12774077915086
55	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white	5.93417908973763
56	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white	5.95714644366655
57	5.6	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	5.79504812715527
58	5.6	0.295	0.2	2.2	0.049	18.0	134.0	0.99378	3.21	0.68	10.0	5	0	white	5.62787747121584
59	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	5.31918848825975
60	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white	6.11991026818632
61	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white	6.03940216993942
62	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white	6.03940216993942
63	5.6	0.54	0.04	1.7	0.049	5.0	13.0	0.9942	3.72	0.58	11.4	5	0	red	5.38667539561888
64	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red	5.3861312852392
65	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	5.57585326122668
66	5.7	0.1	0.27	1.3	0.047	21.0	100.0	0.9928	3.27	0.46	9.5	5	0	white	5.88062375129681
67	5.7	0.16	0.32	1.2	0.036	7.0	89.0	0.99111	3.26	0.48	11.0	5	0	white	6.06777197351312
68	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white	5.91866520912257
69	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white	5.59969998046947
70	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white	5.76729168291723
71	5.7	0.25	0.27	10.8	0.05	58.0	116.0	0.99592	3.1	0.5	9.8	6	0	white	5.73012735856145
72	5.7	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	6.31305151087736
73	5.7	0.28	0.36	1.8	0.041	38.0	90.0	0.99002	3.27	0.98	11.9	7	1	white	6.15514323455778
74	5.7	0.31	0.28	4.1	0.03	22.0	86.0	0.99062	3.31	0.38	11.7	7	1	white	6.15893221875737
75	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white	6.05271546216707
76	5.7	0.39	0.25	4.9	0.033	49.0	113.0	0.98966	3.26	0.58	13.1	7	1	white	6.27777939166302
77	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	5.75282159514865
78	5.8	0.15	0.49	1.1	0.048	21.0	98.0	0.9929	3.19	0.48	9.2	5	0	white	5.79810310458126
79	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	8	1	white	6.20798870633479
80	5.8	0.18	0.37	1.2	0.036	19.0	74.0	0.98853	3.09	0.49	12.7	7	1	white	6.43314497128483
81	5.8	0.19	0.25	10.8	0.042	33.0	124.0	0.99646	3.22	0.41	9.2	6	0	white	5.71464093704915
82	5.8	0.22	0.3	1.1	0.047	36.0	131.0	0.992	3.26	0.45	10.4	5	0	white	5.90699978947481
83	5.8	0.23	0.2	2.0	0.043	39.0	154.0	0.99226	3.21	0.39	10.2	6	0	white	5.91740079648284
84	5.8	0.24	0.28	1.4	0.038	40.0	76.0	0.98711	3.1	0.29	13.9	7	1	white	6.61739020802671
85	5.8	0.24	0.44	3.5	0.029	5.0	109.0	0.9913	3.53	0.43	11.7	3	0	white	6.07507059470385
86	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	6.07468088433731
87	5.8	0.26	0.29	1.0	0.042	35.0	101.0	0.99044	3.36	0.48	11.4	7	1	white	6.10029677386967
88	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	5.87949613167143
89	5.8	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	6.32750497661243
90	5.8	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	6.32750497661243
91	5.8	0.28	0.34	4.0	0.031	40.0	99.0	0.9896	3.39	0.39	12.8	7	1	white	6.32974605025942
92	5.8	0.28	0.35	2.3	0.053	36.0	114.0	0.9924	3.28	0.5	10.2	4	0	white	5.84808298856598
93	5.8	0.29	0.33	3.7	0.029	30.0	88.0	0.98994	3.25	0.42	12.3	6	0	white	6.26139841491886
94	5.8	0.3	0.38	4.9	0.039	22.0	86.0	0.98963	3.23	0.58	13.1	7	1	white	6.34320646912599
95	5.8	0.31	0.31	7.5	0.052	55.0	230.0	0.9949	3.19	0.46	9.8	5	0	white	5.69659775927033
96	5.8	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white	6.12278692431914
97	5.8	0.39	0.47	7.5	0.027	12.0	88.0	0.9907	3.38	0.45	14.0	6	0	white	6.218829035337
98	5.8	0.54	0.0	1.4	0.033	40.0	107.0	0.98918	3.26	0.35	12.4	5	0	white	6.13685931171818
99	5.8	0.68	0.02	1.8	0.087	21.0	94.0	0.9944	3.54	0.52	10.0	5	0	red	5.2892882319648
100	5.8	1.01	0.66	2.0	0.039	15.0	88.0	0.99357	3.66	0.6	11.5	6	0	red	5.06401377485193

Rows: 1-100 | Columns: 15

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.