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verticapy.machine_learning.vertica.linear_model.LinearRegression.predict

LinearRegression.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    | 144.71456 | 6.78086|21.34163 | 0.00000
 fixed_acidity  |  0.13856  | 0.01240|11.17168 | 0.00000
volatile_acidity| -0.74679  | 0.08745|-8.53973 | 0.00000
  citric_acid   | -0.16321  | 0.09442|-1.72857 | 0.08394
 residual_sugar |  0.04173  | 0.00382|10.91472 | 0.00000
   chlorides    | -0.05443  | 0.37811|-0.14397 | 0.88553
    density     |-140.55688 | 6.89726|-20.37867| 0.00000


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


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_mldb_76cff358979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_770946ee979911efa8720242ac120002_"', '"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|5196 

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	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red	5.38169013101381
2	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	5.73155518137344
3	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	6.25007059296192
4	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	5.98375894463129
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	5.98375894463129
6	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	5.53049436274353
7	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	5.84336651177316
8	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	5.52019761631777
9	5.3	0.76	0.03	2.7	0.043	27.0	93.0	0.9932	3.34	0.38	9.2	5	0	white	5.3856958600212
10	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	5.71046762441605
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	6.2289511258877
12	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white	6.01293951087737
13	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	6.20266150541744
14	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white	6.1174229891738
15	5.5	0.24	0.32	8.7	0.06	19.0	102.0	0.994	3.27	0.31	10.4	5	0	white	5.89143167753747
16	5.6	0.2	0.66	10.2	0.043	78.0	175.0	0.9945	2.98	0.43	10.4	7	1	white	5.87291390515554
17	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	5.64405971951845
18	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	6.1187045112795
19	5.6	0.46	0.24	4.8	0.042	24.0	72.0	0.9908	3.29	0.37	12.6	6	0	white	6.04205549590128
20	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	5.52433809736672
21	5.7	0.32	0.5	2.6	0.049	17.0	155.0	0.9927	3.22	0.64	10.0	6	0	white	5.75877770068291
22	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	5.49849699323693
23	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	5.72461052512585
24	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	5.65768705315762
25	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	5.65768705315762
26	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	5.51408027258944
27	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	5.68680792210779
28	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	6.03663391486458
29	5.8	0.36	0.5	1.0	0.127	63.0	178.0	0.99212	3.1	0.45	9.7	5	0	white	5.75326699952592
30	5.9	0.25	0.19	12.4	0.047	50.0	162.0	0.9973	3.35	0.38	9.5	5	0	white	5.6518829367572
31	5.9	0.34	0.31	2.0	0.03	38.0	142.0	0.98892	3.4	0.41	12.9	7	1	white	6.30986243021152
32	6.0	0.17	0.29	5.0	0.028	25.0	108.0	0.99076	3.14	0.34	12.3	6	0	white	6.32061800975617
33	6.0	0.19	0.26	12.4	0.048	50.0	147.0	0.9972	3.3	0.36	8.9	6	0	white	5.71312278801722
34	6.0	0.21	0.34	2.0	0.042	63.0	123.0	0.99052	3.44	0.42	11.4	6	0	white	6.19036049812198
35	6.0	0.26	0.26	2.2	0.035	10.0	72.0	0.989465	3.11	0.48	12.15	6	0	white	6.32309254941839
36	6.0	0.27	0.32	3.6	0.035	36.0	133.0	0.99215	3.23	0.46	10.8	6	0	white	5.98686225446829
37	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	5.62720472519985
38	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	6.13153871060436
39	6.0	0.32	0.3	1.9	0.033	41.0	142.0	0.98912	3.29	0.42	12.8	7	1	white	6.30783805675821
40	6.0	0.34	0.66	15.9	0.046	26.0	164.0	0.9979	3.14	0.5	8.8	6	0	white	5.583603366598
41	6.0	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	5.58333250812416
42	6.0	0.58	0.2	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5	6	0	red	5.36848228027574
43	6.0	0.615	0.04	0.8	0.032	8.0	50.0	0.99036	3.14	0.4	11.0	4	0	white	5.90982686917454
44	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	6.07249916561676
45	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	5.66307471761246
46	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	6.22198825069287
47	6.1	0.25	0.28	10.0	0.055	56.0	131.0	0.994	3.22	0.35	10.9	6	0	white	6.02815012895016
48	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	5.81251691653472
49	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	5.69277038517413
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	5.80235969625866
51	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	5.80235969625866
52	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	5.71241840334082
53	6.1	0.41	0.14	10.4	0.037	18.0	119.0	0.996	3.38	0.45	10.0	5	0	white	5.66807156564806
54	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	5.98488727596293
55	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	5.40307863953956
56	6.1	0.715	0.1	2.6	0.053	13.0	27.0	0.99362	3.57	0.5	11.9	5	0	red	5.45497059310995
57	6.2	0.16	0.54	1.4	0.126	37.0	110.0	0.9932	3.23	0.37	8.9	6	0	white	5.81646547717526
58	6.2	0.26	0.37	7.1	0.047	54.0	201.0	0.99523	3.19	0.48	9.5	6	0	white	5.72637588791378
59	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	6.03237011336091
60	6.2	0.3	0.26	13.4	0.046	57.0	206.0	0.99775	3.17	0.43	9.5	6	0	white	5.62322195206477
61	6.2	0.33	0.29	1.3	0.042	26.0	138.0	0.9956	3.77	0.64	9.5	5	0	white	5.39337559680783
62	6.2	0.35	0.31	2.6	0.036	37.0	92.0	0.98938	3.27	0.53	12.8	7	1	white	6.30401807140467
63	6.2	0.36	0.38	3.2	0.031	20.0	89.0	0.98956	3.06	0.33	12.0	7	1	white	6.2851369794823
64	6.2	0.51	0.14	1.9	0.056	15.0	34.0	0.99396	3.48	0.57	11.5	6	0	red	5.53822487606948
65	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	5.49242910374238
66	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	4.99753903089078
67	6.2	0.76	0.01	3.2	0.041	18.0	120.0	0.99026	3.2	0.94	13.7	7	1	white	5.94787303618645
68	6.3	0.1	0.24	6.0	0.039	25.0	107.0	0.99511	3.59	0.49	10.5	7	1	white	5.85233183904353
69	6.3	0.23	0.33	6.9	0.052	23.0	118.0	0.9938	3.23	0.46	10.4	6	0	white	5.96154126109826
70	6.3	0.25	0.23	14.9	0.039	47.0	142.0	0.99705	3.14	0.35	9.7	6	0	white	5.84068276696576
71	6.3	0.25	0.23	14.9	0.039	47.0	142.0	0.99705	3.14	0.35	9.7	6	0	white	5.84068276696576
72	6.3	0.27	0.25	5.8	0.038	52.0	155.0	0.995	3.28	0.38	9.4	6	0	white	5.73091445389474
73	6.3	0.27	0.25	5.8	0.038	52.0	155.0	0.995	3.28	0.38	9.4	6	0	white	5.73091445389474
74	6.3	0.29	0.4	6.5	0.039	43.0	167.0	0.9953	3.15	0.44	9.3	6	0	white	5.67848865031129
75	6.3	0.32	0.26	12.3	0.044	24.0	205.0	0.99611	3.11	0.58	9.9	5	0	white	5.80685833254873
76	6.3	0.34	0.29	6.2	0.046	29.0	227.0	0.9952	3.29	0.53	10.1	6	0	white	5.6602568440033
77	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	5.79959517148285
78	6.3	0.41	0.18	3.5	0.027	23.0	109.0	0.99018	3.34	0.54	12.8	8	1	white	6.21988672546405
79	6.3	1.02	0.0	2.0	0.083	17.0	24.0	0.99437	3.59	0.55	11.2	4	0	red	5.13914113965495
80	6.4	0.15	0.29	1.8	0.044	21.0	115.0	0.99166	3.1	0.38	10.2	5	0	white	6.13006073106666
81	6.4	0.18	0.32	9.6	0.052	24.0	90.0	0.9963	3.35	0.49	9.4	6	0	white	5.77565368732891
82	6.4	0.29	0.24	3.2	0.037	31.0	95.0	0.98942	2.9	0.66	12.6	7	1	white	6.40732405550557
83	6.4	0.29	0.44	3.6	0.2	75.0	181.0	0.9942	3.02	0.41	9.1	5	0	white	5.71064073602463
84	6.4	0.3	0.36	2.0	0.052	18.0	141.0	0.99273	3.38	0.53	10.5	6	0	white	5.86413263285232
85	6.4	0.31	0.53	8.8	0.057	36.0	221.0	0.99642	3.17	0.44	9.1	5	0	white	5.5937723605669
86	6.4	0.4	0.25	4.2	0.032	15.0	91.0	0.98988	3.26	0.52	13.1	6	0	white	6.30089328900794
87	6.4	0.42	0.46	8.4	0.05	58.0	180.0	0.99495	3.18	0.46	9.7	6	0	white	5.71335655078278
88	6.4	0.56	0.15	1.8	0.078	17.0	65.0	0.99294	3.33	0.6	10.5	6	0	red	5.66496172773219
89	6.5	0.08	0.33	1.9	0.028	23.0	93.0	0.991	3.34	0.7	12.0	7	1	white	6.28747520003412
90	6.5	0.17	0.33	1.4	0.028	14.0	99.0	0.9928	3.23	0.55	10.1	6	0	white	5.94639542006701
91	6.5	0.2	0.35	3.9	0.04	27.0	140.0	0.99102	2.98	0.53	11.8	6	0	white	6.27459642827426
92	6.5	0.21	0.51	17.6	0.045	34.0	125.0	0.99966	3.2	0.47	8.8	6	0	white	5.59806488471614
93	6.5	0.22	0.28	3.7	0.059	29.0	151.0	0.99177	3.23	0.41	12.1	7	1	white	6.15628673311403
94	6.5	0.26	0.28	12.5	0.046	80.0	225.0	0.99685	3.18	0.41	10.0	6	0	white	5.78033845376572
95	6.5	0.27	0.19	6.6	0.045	98.0	175.0	0.99364	3.16	0.34	10.1	6	0	white	5.99258037288686
96	6.5	0.28	0.26	8.8	0.04	44.0	139.0	0.9956	3.32	0.37	10.2	6	0	white	5.79027979385705
97	6.5	0.33	0.38	8.3	0.048	68.0	174.0	0.99492	3.14	0.5	9.6	5	0	white	5.80763235289842
98	6.5	0.36	0.28	3.2	0.037	29.0	119.0	0.9908	3.25	0.65	12.4	8	1	white	6.16840750710136
99	6.5	0.36	0.31	13.55	0.053	20.0	113.0	0.99544	3.2	0.56	11.0	6	0	white	5.94238621482944
100	6.5	0.36	0.36	6.7	0.185	51.5	151.0	0.99528	3.17	0.42	9.3	5	0	white	5.66366295263498

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.