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verticapy.machine_learning.vertica.linear_model.ElasticNet.predict

ElasticNet.predict(vdf: Annotated[str | vDataFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | 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	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	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
3	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
4	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
5	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
6	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
7	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
8	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
9	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
10	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
11	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
12	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
13	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
14	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
15	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
16	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
17	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
18	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
19	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
20	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
21	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
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	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
26	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
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	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
29	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
30	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
31	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
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	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
34	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
35	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
36	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
37	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
38	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
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	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
42	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
43	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
44	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
45	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
46	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
47	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
48	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
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	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
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	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
71	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
72	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
73	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
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	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
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	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
81	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
82	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
83	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
84	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
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	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
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	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

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,
)



=======
details
=======
   predictor    |coefficient|std_err | t_value |p_value 
----------------+-----------+--------+---------+--------
   Intercept    | 150.90870 | 6.81653|22.13863 | 0.00000
 fixed_acidity  |  0.14868  | 0.01239|12.00417 | 0.00000
volatile_acidity| -0.72027  | 0.08893|-8.09901 | 0.00000
  citric_acid   | -0.14729  | 0.09512|-1.54843 | 0.12158
 residual_sugar |  0.04347  | 0.00382|11.37399 | 0.00000
   chlorides    | -0.29938  | 0.38213|-0.78344 | 0.43340
    density     |-146.87304 | 6.93337|-21.18351| 0.00000


==============
regularization
==============
type| lambda 
----+--------
none| 1.00000


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_mldb_2c0650c4979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_2c55ac1e979911efa8720242ac120002_"', '"quality"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"'
USING PARAMETERS optimizer='newton', epsilon=1e-06, max_iterations=100, regularization='none', lambda=1, alpha=0.5, fit_intercept=true)

===============
Additional Info
===============
       Name       |Value
------------------+-----
 iteration_count  |  1  
rejected_row_count|  0  
accepted_row_count|5194 

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	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white	6.19733526832081
2	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	6.25687772912599
3	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	6.22646618920612
4	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	5.870229275648
5	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	5.98160369173721
6	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	5.83181691973158
7	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	6.02946049866898
8	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	5.98887961276421
9	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white	5.68509709922199
10	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	5.83800907915312
11	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	6.24014178141303
12	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	5.80510775571426
13	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	6.03847433499996
14	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	6.156653761001
15	5.6	0.27	0.37	0.9	0.025	11.0	49.0	0.98845	3.29	0.33	13.1	6	0	white	6.34734940878536
16	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	5.95556642540717
17	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	5.30483764217684
18	5.6	0.41	0.22	7.1	0.05	44.0	154.0	0.9931	3.3	0.4	10.5	5	0	white	5.84768810565484
19	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	5.50552198160784
20	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	5.5974455506211
21	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	5.6159031702168
22	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	5.65010189256816
23	5.7	0.36	0.21	6.7	0.038	51.0	166.0	0.9941	3.29	0.63	10.0	6	0	white	5.73937354622956
24	5.7	0.37	0.3	1.1	0.029	24.0	88.0	0.98883	3.18	0.39	11.7	6	0	white	6.25218669931655
25	5.7	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	5.49101532918118
26	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	5.70121313022923
27	5.8	0.2	0.24	1.4	0.033	65.0	169.0	0.99043	3.59	0.56	12.3	7	1	white	6.17518544324111
28	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	5.49711999908638
29	5.8	0.32	0.31	2.7	0.049	25.0	153.0	0.99067	3.44	0.73	12.2	7	1	white	6.09491690531502
30	5.8	0.36	0.26	3.3	0.038	40.0	153.0	0.9911	3.34	0.55	11.3	6	0	white	6.03969173407808
31	5.9	0.17	0.28	0.7	0.027	5.0	28.0	0.98985	3.13	0.32	10.6	5	0	white	6.26232246691347
32	5.9	0.23	0.3	12.9	0.054	57.0	170.0	0.9972	3.28	0.39	9.4	5	0	white	5.65891889783225
33	5.9	0.32	0.19	14.5	0.042	37.0	115.0	0.99684	3.16	0.43	10.3	5	0	white	5.73631895327586
34	6.0	0.19	0.29	1.2	0.046	29.0	92.0	0.99033	3.22	0.53	11.3	6	0	white	6.20686144939535
35	6.0	0.2	0.32	3.0	0.031	26.0	118.0	0.99134	3.38	0.68	11.2	7	1	white	6.12963845852406
36	6.0	0.28	0.52	6.2	0.028	37.0	104.0	0.99161	3.28	0.51	11.8	7	1	white	6.14291136914051
37	6.0	0.34	0.32	3.8	0.044	13.0	116.0	0.99108	3.39	0.44	11.8	7	1	white	6.09787367829938
38	6.0	0.36	0.39	3.2	0.027	20.0	125.0	0.991	3.38	0.39	11.3	7	1	white	6.06391389557436
39	6.0	0.41	0.23	1.1	0.066	22.0	148.0	0.99266	3.3	0.47	9.63333333333333	5	0	white	5.70469118357687
40	6.0	0.5	0.04	2.2	0.092	13.0	26.0	0.99647	3.46	0.47	10.0	5	0	red	5.14830176168897
41	6.0	0.52	0.33	7.7	0.046	24.0	119.0	0.99224	3.04	0.38	11.0	6	0	white	5.96532186540256
42	6.1	0.21	0.4	1.4	0.066	40.5	165.0	0.9912	3.25	0.59	11.9	6	0	red	6.06604973976411
43	6.1	0.24	0.25	1.6	0.044	24.0	115.0	0.9921	3.39	0.59	10.9	6	0	white	5.94963048233961
44	6.1	0.24	0.27	9.8	0.062	33.0	152.0	0.9966	3.31	0.47	9.5	6	0	white	5.63683754683626
45	6.1	0.24	0.32	9.0	0.031	41.0	134.0	0.99234	3.25	0.26	12.3	7	1	white	6.22965514274503
46	6.1	0.26	0.51	2.2	0.05	61.0	154.0	0.9929	3.08	0.6	9.8	6	0	white	5.80371763556028
47	6.1	0.31	0.26	2.2	0.051	28.0	167.0	0.9926	3.37	0.47	10.4	6	0	white	5.84828986378662
48	6.1	0.32	0.28	6.6	0.021	29.0	132.0	0.99188	3.15	0.36	11.45	7	1	white	6.14414798391101
49	6.1	0.32	0.37	1.8	0.051	13.0	200.0	0.9945	3.49	0.44	10.5	4	0	white	5.52843745372584
50	6.1	0.34	0.21	5.0	0.042	17.0	133.0	0.99373	3.02	0.53	9.4	5	0	white	5.7924958792328
51	6.1	0.34	0.24	18.35	0.05	33.0	184.0	0.99943	3.12	0.61	9.3	5	0	white	5.52885690498843
52	6.1	0.34	0.25	1.8	0.084	4.0	28.0	0.99464	3.36	0.44	10.1	5	0	red	5.50126555069824
53	6.1	0.34	0.29	2.2	0.036	25.0	100.0	0.98938	3.06	0.44	11.8	6	0	white	6.29968489446574
54	6.1	0.34	0.46	4.7	0.029	21.0	94.0	0.991	3.29	0.62	12.3	6	0	white	6.14748649239738
55	6.1	0.35	0.07	1.4	0.069	22.0	108.0	0.9934	3.23	0.52	9.2	5	0	white	5.68979989991871
56	6.1	0.4	0.16	1.8	0.069	11.0	25.0	0.9955	3.42	0.74	10.1	7	1	red	5.34948561477316
57	6.1	0.41	0.0	1.6	0.063	36.0	87.0	0.9914	3.27	0.67	10.8	6	0	white	5.96113103567271
58	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.3	7	1	white	5.99338798453746
59	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.2666666666667	7	1	white	5.99338798453746
60	6.1	0.56	0.0	2.2	0.079	6.0	9.0	0.9948	3.59	0.54	11.5	6	0	red	5.3750157173875
61	6.2	0.2	0.29	11.8	0.035	21.0	93.0	0.99364	3.18	0.34	11.9	6	0	white	6.2073422134722
62	6.2	0.2	0.31	1.0	0.031	22.0	73.0	0.99035	3.24	0.52	11.3	6	0	white	6.21930862629191
63	6.2	0.23	0.35	0.7	0.051	24.0	111.0	0.9916	3.37	0.43	11.0	3	0	white	5.9891884852718
64	6.2	0.24	0.25	12.5	0.055	47.0	134.0	0.99758	3.3	0.51	9.0	5	0	white	5.63018629228051
65	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	6.02995273653269
66	6.2	0.3	0.21	1.1	0.032	31.0	111.0	0.9889	2.97	0.42	12.2	6	0	white	6.37902481898732
67	6.2	0.33	0.14	4.8	0.052	27.0	128.0	0.99475	3.21	0.48	9.4	5	0	white	5.66337882083118
68	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	6.47692314772115
69	6.2	0.53	0.02	0.9	0.035	6.0	81.0	0.99234	3.24	0.35	9.5	4	0	white	5.72651298788125
70	6.2	0.56	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.6	11.3	5	0	red	5.47723661605085
71	6.2	0.56	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.6	11.3	5	0	red	5.47723661605085
72	6.2	0.63	0.31	1.7	0.088	15.0	64.0	0.9969	3.46	0.79	9.3	5	0	red	4.96094105306392
73	6.3	0.15	0.34	11.4	0.05	25.0	96.0	0.99754	3.21	0.49	10.0	6	0	white	5.6561751476554
74	6.3	0.16	0.4	1.6	0.033	59.0	148.0	0.9914	3.44	0.53	11.4	5	0	white	6.12099924123524
75	6.3	0.21	0.28	1.5	0.051	46.0	142.0	0.9928	3.23	0.42	10.1	6	0	white	5.88730270979292
76	6.3	0.22	0.33	1.7	0.041	67.0	164.0	0.9928	3.32	0.56	10.4	6	0	white	5.88442357350056
77	6.3	0.23	0.31	1.5	0.022	11.0	82.0	0.9892	3.3	0.4	12.9	7	1	white	6.40590343440763
78	6.3	0.255	0.37	1.1	0.04	37.0	114.0	0.9905	3.0	0.39	10.9	6	0	white	6.16534668565498
79	6.3	0.26	0.29	2.2	0.043	35.0	175.0	0.9918	3.38	0.43	11.6	6	0	white	6.02951484506514
80	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	5.62987203366836
81	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	5.62987203366836
82	6.3	0.3	0.28	5.0	0.042	36.0	168.0	0.99505	3.22	0.69	9.5	6	0	white	5.64686063524442
83	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	5.78728396374399
84	6.3	0.41	0.3	3.2	0.03	49.0	164.0	0.9927	3.53	0.79	11.7	7	1	white	5.83517990420182
85	6.3	0.41	0.3	3.2	0.03	49.0	164.0	0.9927	3.53	0.79	11.7	7	1	white	5.83517990420182
86	6.3	0.48	0.04	1.1	0.046	30.0	99.0	0.9928	3.24	0.36	9.6	6	0	white	5.71228865687118
87	6.4	0.12	0.3	1.1	0.031	37.0	94.0	0.98986	3.01	0.56	11.7	6	0	white	6.38445488255942
88	6.4	0.125	0.36	1.4	0.044	22.0	68.0	0.99014	3.15	0.5	11.7	7	1	white	6.34004126721726
89	6.4	0.15	0.36	1.8	0.034	43.0	150.0	0.9922	3.42	0.69	11.0	8	1	white	6.03985866278035
90	6.4	0.19	0.42	2.9	0.032	32.0	83.0	0.9908	3.3	0.41	11.7	6	0	white	6.2562506203719
91	6.4	0.2	0.15	6.6	0.046	26.0	113.0	0.99408	2.99	0.58	9.9	6	0	white	5.9637284122615
92	6.4	0.23	0.37	7.9	0.05	60.0	150.0	0.99488	2.86	0.49	9.3	6	0	white	5.84753372906758
93	6.4	0.26	0.22	5.1	0.037	23.0	131.0	0.9944	3.29	0.32	10.1	5	0	white	5.80068890118488
94	6.4	0.26	0.25	10.7	0.046	66.0	179.0	0.99606	3.17	0.55	9.9	6	0	white	5.79320967549236
95	6.4	0.27	0.32	4.5	0.24	61.0	174.0	0.9948	3.12	0.48	9.4	5	0	white	5.63315130018171
96	6.4	0.29	0.3	2.9	0.036	25.0	79.0	0.99037	3.29	0.6	12.4	7	1	white	6.2638568294787
97	6.4	0.41	0.01	6.1	0.048	20.0	70.0	0.99362	3.19	0.42	10.0	5	0	white	5.87831996175106
98	6.4	0.45	0.07	1.1	0.03	10.0	131.0	0.9905	2.97	0.28	10.8	5	0	white	6.08694439066977
99	6.4	0.475	0.06	1.0	0.03	9.0	131.0	0.9904	2.97	0.29	10.8	5	0	white	6.08075072507734
100	6.4	0.55	0.26	9.6	0.027	20.0	104.0	0.9924	3.22	0.73	13.1	6	0	white	6.07828338776028

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.