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verticapy.machine_learning.vertica.ensemble.XGBRegressor.predict

XGBRegressor.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    | 148.42552 | 6.73724|22.03062 | 0.00000
 fixed_acidity  |  0.13441  | 0.01236|10.87627 | 0.00000
volatile_acidity| -0.68741  | 0.08710|-7.89238 | 0.00000
  citric_acid   | -0.10147  | 0.09543|-1.06327 | 0.28771
 residual_sugar |  0.04281  | 0.00381|11.23134 | 0.00000
   chlorides    | -0.12376  | 0.39072|-0.31675 | 0.75145
    density     |-144.30539 | 6.85321|-21.05663| 0.00000


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


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_mldb_ec666b66979811efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_ecaa485e979811efa8720242ac120002_"', '"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|5197 

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	5.69568988220047
2	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	5.78538530749046
3	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	5.79481020254627
4	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	5.49361403991517
5	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	6.16567231736551
6	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	6.29263610674406
7	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	6.05490516646978
8	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	5.9987611258054
9	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	6.03445335474936
10	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	5.7669174931498
11	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	5.86950740811469
12	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	5.95326781766136
13	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	5.7355170353205
14	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	6.37155462069256
15	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	5.66628709129407
16	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	6.10872263681338
17	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	6.13919864666474
18	5.8	0.61	0.01	8.4	0.041	31.0	104.0	0.9909	3.26	0.72	14.05	7	1	white	6.14710194893038
19	5.9	0.22	0.18	6.4	0.041	28.0	120.0	0.99403	3.27	0.5	9.9	5	0	white	5.87408185112776
20	5.9	0.34	0.3	3.8	0.035	57.0	135.0	0.99016	3.09	0.34	12.0	6	0	white	6.22730520103644
21	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	5.63666079751755
22	5.9	0.62	0.28	3.5	0.039	55.0	152.0	0.9907	3.44	0.44	12.0	6	0	white	5.94559459008451
23	6.0	0.2	0.26	6.8	0.049	22.0	93.0	0.9928	3.15	0.42	11.0	6	0	white	6.08678443208936
24	6.0	0.21	0.24	12.1	0.05	55.0	164.0	0.997	3.34	0.39	9.4	5	0	white	5.70264447821137
25	6.0	0.25	0.4	5.7	0.052	56.0	152.0	0.99398	3.16	0.88	10.5	6	0	white	5.82046145937301
26	6.0	0.26	0.24	1.3	0.053	66.0	150.0	0.9924	3.21	0.62	10.4	6	0	white	5.86932220139019
27	6.0	0.28	0.27	15.5	0.036	31.0	134.0	0.99408	3.19	0.44	13.0	7	1	white	6.22015142600458
28	6.0	0.28	0.27	15.5	0.036	31.0	134.0	0.99408	3.19	0.44	13.0	7	1	white	6.22015142600458
29	6.0	0.3	0.33	2.1	0.042	31.0	127.0	0.98964	3.32	0.42	12.5	6	0	white	6.26658829691618
30	6.0	0.36	0.16	6.3	0.036	36.0	191.0	0.9942	3.17	0.62	9.8	5	0	white	5.7651197501539
31	6.0	0.37	0.32	1.0	0.053	31.0	218.5	0.9924	3.29	0.72	9.8	6	0	white	5.77274496906128
32	6.0	0.39	0.17	12.0	0.046	65.0	246.0	0.9976	3.15	0.38	9.0	6	0	white	5.49564325704128
33	6.0	0.4	0.3	1.6	0.047	30.0	117.0	0.9931	3.17	0.48	10.1	6	0	white	5.67956878184535
34	6.0	0.555	0.26	4.5	0.053	17.0	126.0	0.9943	3.24	0.46	9.1	5	0	white	5.5273282877402
35	6.0	0.59	0.0	0.8	0.037	30.0	95.0	0.99032	3.1	0.4	10.9	4	0	white	5.94755695275882
36	6.1	0.14	0.25	1.3	0.047	37.0	173.0	0.9925	3.35	0.46	10.0	6	0	white	5.95055022035271
37	6.1	0.15	0.4	1.2	0.03	19.0	84.0	0.98926	3.19	0.96	13.0	6	0	white	6.39382758990175
38	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	5.8359802473031
39	6.1	0.26	0.25	2.9	0.047	289.0	440.0	0.99314	3.44	0.64	10.5	3	0	white	5.84420653429444
40	6.1	0.27	0.3	16.7	0.039	49.0	172.0	0.99985	3.4	0.45	9.4	5	0	white	5.45578515378367
41	6.1	0.36	0.41	19.35	0.07	67.0	207.0	1.00118	3.39	0.53	9.1	5	0	white	5.30044901926709
42	6.15	0.21	0.37	3.2	0.021	20.0	80.0	0.99076	3.39	0.47	12.0	5	0	white	6.23262993114707
43	6.2	0.16	0.47	1.4	0.029	23.0	81.0	0.99	3.26	0.42	12.2	6	0	white	6.29519196033061
44	6.2	0.255	0.24	1.7	0.039	138.5	272.0	0.99452	3.53	0.53	9.6	4	0	white	5.61257179231637
45	6.2	0.255	0.27	1.3	0.037	30.0	86.0	0.98834	3.05	0.59	12.9	7	1	white	6.48445717147851
46	6.2	0.28	0.27	10.3	0.03	26.0	108.0	0.99388	3.2	0.36	10.7	6	0	white	6.05400715158555
47	6.2	0.3	0.49	11.2	0.058	68.0	215.0	0.99656	3.19	0.6	9.4	6	0	white	5.66626378625963
48	6.2	0.34	0.3	11.1	0.047	28.0	237.0	0.9981	3.18	0.49	8.7	5	0	white	5.43289625226785
49	6.2	0.52	0.08	4.4	0.071	11.0	32.0	0.99646	3.56	0.63	11.6	6	0	red	5.27832571579847
50	6.3	0.21	0.29	11.7	0.048	49.0	147.0	0.99482	3.22	0.38	10.8	5	0	white	6.03560179956921
51	6.3	0.22	0.27	4.5	0.036	81.0	157.0	0.9928	3.05	0.76	10.7	7	1	white	6.01548234022451
52	6.3	0.22	0.34	1.2	0.036	32.0	96.0	0.98961	3.06	0.74	11.6	6	0	white	6.32742930049614
53	6.3	0.22	0.43	4.55	0.038	31.0	130.0	0.9918	3.35	0.33	11.5	7	1	white	6.14544565385901
54	6.3	0.23	0.33	1.5	0.036	15.0	105.0	0.991	3.32	0.42	11.2	6	0	white	6.13382939002355
55	6.3	0.26	0.42	7.1	0.045	62.0	209.0	0.99544	3.2	0.53	9.5	6	0	white	5.70200008496781
56	6.3	0.3	0.34	1.6	0.049	14.0	132.0	0.994	3.3	0.49	9.5	6	0	white	5.65445196598941
57	6.3	0.3	0.91	8.2	0.034	50.0	199.0	0.99394	3.39	0.49	11.7	6	0	white	5.88969732764994
58	6.3	0.36	0.28	2.5	0.035	18.0	73.0	0.98868	3.1	0.47	12.8	7	1	white	6.42726473816191
59	6.3	0.37	0.28	6.3	0.034	45.0	152.0	0.9921	3.29	0.46	11.6	7	1	white	6.08968095474694
60	6.3	0.37	0.51	6.3	0.048	35.0	146.0	0.9943	3.1	1.01	10.5	6	0	white	5.74713830519391
61	6.3	0.48	0.48	1.8	0.035	35.0	96.0	0.99121	3.49	0.74	12.2	6	0	white	5.92941891299589
62	6.4	0.12	0.49	6.4	0.042	49.0	161.0	0.9945	3.34	0.44	10.4	6	0	white	5.91062510748469
63	6.4	0.14	0.28	7.9	0.057	21.0	82.0	0.99425	3.26	0.36	10.0	6	0	white	6.01662567427908
64	6.4	0.16	0.37	1.5	0.037	27.0	109.0	0.99345	3.38	0.5	9.8	6	0	white	5.83765858838433
65	6.4	0.18	0.74	11.9	0.046	54.0	168.0	0.9978	3.58	0.68	10.1	5	0	white	5.60278373965841
66	6.4	0.21	0.3	5.6	0.044	43.0	160.0	0.9949	3.6	0.41	10.6	6	0	white	5.77581672699367
67	6.4	0.21	0.5	11.6	0.042	45.0	153.0	0.9972	3.15	0.43	8.8	5	0	white	5.68074840293247
68	6.4	0.22	0.34	1.4	0.023	56.0	115.0	0.98958	3.18	0.7	11.7	6	0	white	6.35537099675685
69	6.4	0.25	0.3	5.5	0.038	15.0	129.0	0.9948	3.14	0.49	9.6	6	0	white	5.7592119030667
70	6.4	0.31	0.4	6.4	0.039	39.0	191.0	0.99513	3.14	0.52	9.8	5	0	white	5.69860756876022
71	6.4	0.32	0.23	16.2	0.055	36.0	176.0	0.9986	3.26	0.54	9.1	5	0	white	5.62583515070654
72	6.4	0.32	0.27	4.9	0.034	18.0	122.0	0.9916	3.36	0.71	12.5	6	0	white	6.15072124150629
73	6.4	0.38	0.19	4.5	0.038	36.0	119.0	0.99151	3.07	0.42	11.2	6	0	white	6.11296106830488
74	6.4	0.44	0.26	2.0	0.054	20.0	180.0	0.9952	3.58	0.57	10.0	5	0	white	5.42311264454577
75	6.5	0.13	0.27	2.6	0.035	32.0	76.0	0.9914	3.21	0.76	11.3333333333333	6	0	white	6.22503737400271
76	6.5	0.18	0.26	1.4	0.041	40.0	141.0	0.9941	3.34	0.72	9.5	6	0	white	5.74993811146149
77	6.5	0.22	0.45	8.0	0.053	52.0	196.0	0.9959	3.23	0.48	9.1	6	0	white	5.72449604010419
78	6.5	0.22	0.72	6.8	0.042	33.0	168.0	0.9958	3.12	0.36	9.2	6	0	white	5.66151485340663
79	6.5	0.23	0.25	17.3	0.046	15.0	110.0	0.99828	3.15	0.42	9.2	6	0	white	5.79350035754911
80	6.5	0.23	0.45	2.1	0.027	43.0	104.0	0.99054	3.02	0.52	11.3	6	0	white	6.24171729214927
81	6.5	0.24	0.29	8.2	0.043	32.0	156.0	0.99453	3.13	0.7	10.1	6	0	white	5.93448165811805
82	6.5	0.25	0.27	15.2	0.049	75.0	217.0	0.9972	3.19	0.39	9.9	5	0	white	5.84329300056956
83	6.5	0.26	0.34	1.4	0.04	25.0	184.0	0.99216	3.29	0.46	10.7	5	0	white	5.96690356732523
84	6.5	0.26	0.34	1.4	0.04	25.0	184.0	0.99216	3.29	0.46	10.7	5	0	white	5.96690356732523
85	6.5	0.28	0.25	4.8	0.029	54.0	128.0	0.99074	3.17	0.44	12.2	7	1	white	6.31412829059752
86	6.5	0.28	0.27	5.2	0.04	44.0	179.0	0.9948	3.19	0.69	9.4	6	0	white	5.74198300348593
87	6.5	0.28	0.29	2.7	0.038	26.0	107.0	0.9912	3.32	0.41	11.6	7	1	white	6.15266695294244
88	6.5	0.28	0.38	7.8	0.031	54.0	216.0	0.99154	3.03	0.42	13.1	6	0	white	6.31368563220238
89	6.5	0.29	0.31	1.7	0.035	24.0	79.0	0.99053	3.27	0.69	11.4	7	1	white	6.19800586264176
90	6.5	0.29	0.42	10.6	0.042	66.0	202.0	0.99674	3.24	0.53	9.5	6	0	white	5.67088088984437
91	6.5	0.33	0.72	1.1	0.061	7.0	151.0	0.993	3.09	0.57	9.5	4	0	white	5.74356636812544
92	6.5	0.41	0.22	4.8	0.052	49.0	142.0	0.9946	3.14	0.62	9.2	5	0	white	5.6679432336897
93	6.6	0.085	0.33	1.4	0.036	17.0	109.0	0.99306	3.27	0.61	9.5	6	0	white	5.9722769482353
94	6.6	0.15	0.34	1.0	0.037	45.0	79.0	0.98949	2.96	0.5	11.7	6	0	white	6.42450143626905
95	6.6	0.22	0.23	17.3	0.047	37.0	118.0	0.99906	3.08	0.46	8.8	6	0	white	5.70316291386945
96	6.6	0.23	0.3	14.9	0.051	33.0	118.0	0.99835	3.04	0.54	9.0	6	0	white	5.68839540432404
97	6.6	0.24	0.27	15.8	0.035	46.0	188.0	0.9982	3.24	0.51	9.2	5	0	white	5.74672341739716
98	6.6	0.25	0.31	12.4	0.059	52.0	181.0	0.9984	3.51	0.47	9.8	6	0	white	5.55839348237359
99	6.6	0.25	0.36	8.1	0.045	54.0	180.0	0.9958	3.08	0.42	9.2	5	0	white	5.74614886064668
100	6.6	0.25	0.42	11.3	0.049	77.0	231.0	0.9966	3.24	0.52	9.5	6	0	white	5.76112428540449

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.