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verticapy.machine_learning.vertica.tree.DecisionTreeRegressor.predict

DecisionTreeRegressor.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    | 149.80642 | 6.83430|21.91978 | 0.00000
 fixed_acidity  |  0.14534  | 0.01249|11.63893 | 0.00000
volatile_acidity| -0.73376  | 0.08752|-8.38347 | 0.00000
  citric_acid   | -0.16034  | 0.09518|-1.68460 | 0.09213
 residual_sugar |  0.04545  | 0.00385|11.81257 | 0.00000
   chlorides    |  0.00542  | 0.37950| 0.01428 | 0.98861
    density     |-145.75416 | 6.95208|-20.96554| 0.00000


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


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_mldb_c0547b72979c11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_c0a0d71a979c11efa8720242ac120002_"', '"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|5193 

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	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	5.86329239646642
2	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	6.1484843454582
3	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	6.28138792486973
4	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	5.24221955837967
5	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	6.12789772149205
6	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	5.91836708324547
7	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	6.22586695414424
8	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	6.22586695414424
9	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	5.84353944111143
10	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	5.93469230809893
11	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	6.01518634254788
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	5.9471381812331
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	5.73260241911947
14	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	6.00641707985341
15	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	6.36658187013205
16	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	5.61568022587085
17	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	5.61568022587085
18	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	5.61568022587085
19	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	5.67425557779697
20	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	6.20452513845089
21	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	5.90195549836881
22	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	6.1273053354185
23	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	6.1273053354185
24	5.8	0.36	0.38	0.9	0.037	3.0	75.0	0.9904	3.28	0.34	11.4	4	0	white	6.01046511465628
25	5.9	0.14	0.25	4.5	0.027	34.0	140.0	0.9934	3.49	0.51	10.8	6	0	white	5.93355639291735
26	5.9	0.19	0.37	0.8	0.027	3.0	21.0	0.9897	3.09	0.31	10.8	5	0	white	6.24877077286391
27	5.9	0.21	0.23	7.9	0.033	22.0	130.0	0.9944	3.38	0.59	10.9	6	0	white	5.89419182777584
28	5.9	0.23	0.24	3.8	0.038	61.0	152.0	0.99139	3.31	0.5	11.3	7	1	white	6.13033510016896
29	5.9	0.24	0.34	2.0	0.037	40.0	108.0	0.98948	3.19	0.5	12.3	6	0	white	6.30354720671801
30	5.9	0.33	0.32	8.1	0.038	9.0	34.0	0.9911	3.22	0.36	12.7	7	1	white	6.28181470981406
31	5.9	0.46	0.14	2.7	0.042	27.0	160.0	0.9931	3.46	0.51	10.6	7	1	white	5.6783962817974
32	6.0	0.26	0.34	1.3	0.046	6.0	29.0	0.9924	3.29	0.63	10.4	5	0	white	5.84604053629431
33	6.0	0.27	0.19	1.7	0.02	24.0	110.0	0.9898	3.32	0.47	12.6	7	1	white	6.25975191720616
34	6.0	0.28	0.22	12.15	0.048	42.0	163.0	0.9957	3.2	0.46	10.1	5	0	white	5.86270835879006
35	6.0	0.33	0.38	9.7	0.04	29.0	124.0	0.9954	3.47	0.48	11.0	6	0	white	5.7327081031043
36	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	5.75493793586597
37	6.0	0.39	0.26	2.7	0.038	39.0	187.0	0.99325	3.41	0.5	10.8	6	0	white	5.70316757731666
38	6.0	0.41	0.21	1.9	0.05	29.0	122.0	0.9928	3.42	0.52	10.5	6	0	white	5.72580760091128
39	6.1	0.105	0.31	1.3	0.037	55.0	145.0	0.9912	3.41	0.41	11.1	7	1	white	6.15397358751454
40	6.1	0.22	0.23	3.1	0.052	15.0	104.0	0.9948	3.14	0.42	8.7	5	0	white	5.6395858065388
41	6.1	0.22	0.4	1.85	0.031	25.0	111.0	0.98966	3.03	0.3	11.8	7	1	white	6.3045841530633
42	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	5.81668804921736
43	6.1	0.28	0.35	12.8	0.048	63.0	229.0	0.9975	3.08	0.4	8.9	5	0	white	5.62357961101279
44	6.1	0.3	0.3	2.1	0.031	50.0	163.0	0.9895	3.39	0.43	12.7	7	1	white	6.29659917758786
45	6.1	0.3	0.47	1.4	0.049	50.0	187.0	0.9927	3.19	0.45	9.5	5	0	white	5.77121397925359
46	6.1	0.37	0.36	4.7	0.035	36.0	116.0	0.991	3.31	0.62	12.6	6	0	white	6.13516335631044
47	6.1	0.45	0.27	0.8	0.039	13.0	82.0	0.9927	3.23	0.32	9.5	5	0	white	5.6658964289951
48	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	6.22496597395624
49	6.2	0.15	0.27	1.4	0.041	51.0	117.0	0.9909	3.28	0.38	11.2	6	0	white	6.19019392923198
50	6.2	0.18	0.38	1.5	0.028	36.0	117.0	0.993	3.47	0.54	9.7	6	0	white	5.84893401506523
51	6.2	0.22	0.3	12.4	0.054	108.0	152.0	0.99728	3.1	0.47	9.5	6	0	white	5.70407623872066
52	6.2	0.32	0.32	2.2	0.036	15.0	70.0	0.9899	3.16	0.48	12.7	6	0	white	6.23952066756999
53	6.2	0.34	0.28	7.5	0.034	40.0	197.0	0.99485	3.14	0.6	9.7	5	0	white	5.75062451873566
54	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	5.43738976789899
55	6.2	0.57	0.1	2.1	0.048	4.0	11.0	0.99448	3.44	0.76	10.8	6	0	red	5.4193214861601
56	6.2	0.66	0.48	1.2	0.029	29.0	75.0	0.9892	3.33	0.39	12.8	8	1	white	6.02093213217671
57	6.3	0.17	0.23	5.7	0.048	44.0	147.0	0.99382	3.08	0.54	10.0	5	0	white	5.96631590151162
58	6.3	0.17	0.32	4.2	0.04	37.0	117.0	0.99182	3.24	0.43	11.3	6	0	white	6.17518251925387
59	6.3	0.23	0.21	5.1	0.035	29.0	142.0	0.9942	3.36	0.33	10.1	7	1	white	5.8427728692786
60	6.3	0.28	0.22	9.5	0.04	30.0	111.0	0.99338	3.05	0.31	10.8	4	0	white	6.12398567470078
61	6.3	0.29	0.23	14.2	0.037	24.0	99.0	0.99528	3.08	0.38	10.6	6	0	white	6.0516878206995
62	6.3	0.29	0.29	3.3	0.037	32.0	140.0	0.9895	3.17	0.36	12.8	7	1	white	6.38917406787388
63	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	5.85794230996922
64	6.3	0.39	0.35	5.9	0.04	82.5	260.0	0.9941	3.12	0.66	10.1	5	0	white	5.75388207049485
65	6.3	0.4	0.32	10.6	0.049	38.0	209.0	0.9981	3.47	0.59	9.3	6	0	white	5.38197909605603
66	6.3	0.55	0.45	13.0	0.047	33.0	182.0	0.9974	3.2	0.46	9.2	6	0	white	5.46215616169306
67	6.3	0.76	0.0	2.9	0.072	26.0	52.0	0.99379	3.51	0.6	11.5	6	0	red	5.44753091422021
68	6.4	0.16	0.32	8.75	0.038	38.0	118.0	0.99449	3.19	0.41	10.7	5	0	white	6.01465480789375
69	6.4	0.17	0.27	9.9	0.047	26.0	101.0	0.99596	3.34	0.5	9.9	6	0	white	5.85338629041803
70	6.4	0.17	0.34	1.5	0.091	42.0	135.0	0.9938	3.25	0.49	9.6	7	1	white	5.77549043642605
71	6.4	0.18	0.48	4.0	0.186	64.0	150.0	0.9945	3.06	0.4	9.3	5	0	white	5.75780504887268
72	6.4	0.21	0.44	7.4	0.045	47.0	182.0	0.9957	3.24	0.46	9.1	5	0	white	5.72105033944086
73	6.4	0.22	0.38	9.1	0.044	35.0	127.0	0.99326	2.97	0.3	11.0	7	1	white	6.15622466115076
74	6.4	0.23	0.26	8.1	0.054	47.0	181.0	0.9954	3.12	0.49	9.4	5	0	white	5.810822938679
75	6.4	0.24	0.31	2.8	0.038	41.0	114.0	0.99155	3.37	0.66	11.7	7	1	white	6.11567571920855
76	6.4	0.28	0.56	1.7	0.156	49.0	106.0	0.99354	3.1	0.37	9.2	6	0	white	5.70683921663462
77	6.4	0.31	0.4	6.2	0.04	46.0	169.0	0.9953	3.15	0.46	9.3	6	0	white	5.65782813415692
78	6.4	0.32	0.26	7.9	0.05	53.0	180.0	0.99514	3.14	0.5	9.6	5	0	white	5.77356976456264
79	6.4	0.37	0.12	5.9	0.056	6.0	91.0	0.99536	3.06	0.46	8.4	4	0	white	5.63640551889148
80	6.4	0.4	0.19	3.2	0.033	28.0	124.0	0.9904	3.22	0.54	12.7	7	1	white	6.20328291729089
81	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	5.4086682538011
82	6.5	0.21	0.37	2.5	0.048	70.0	138.0	0.9917	3.33	0.75	11.4	7	1	white	6.1071592574994
83	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	5.72480252276233
84	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	5.82524019388066
85	6.5	0.25	0.2	1.4	0.024	29.0	101.0	0.9916	3.24	0.54	10.8	6	0	white	6.06952225127438
86	6.5	0.28	0.28	20.4	0.041	40.0	144.0	1.0002	3.14	0.38	8.7	5	0	white	5.64474687267415
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	6.15053506763567
88	6.5	0.3	0.39	7.8	0.038	61.0	219.0	0.9959	3.19	0.5	9.4	5	0	white	5.66655167085841
89	6.5	0.32	0.23	8.5	0.051	20.0	138.0	0.9943	3.03	0.42	10.7	5	0	white	5.94261956011272
90	6.5	0.36	0.32	1.1	0.031	13.0	66.0	0.9916	3.1	0.46	10.6	5	0	white	5.95597205944895
91	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	5.66631642985902
92	6.6	0.16	0.36	1.1	0.031	27.0	93.0	0.98884	3.23	0.34	13.2	8	1	white	6.5131258537748
93	6.6	0.21	0.39	2.3	0.041	31.0	102.0	0.99221	3.22	0.58	10.9	7	1	white	6.03502444235178
94	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	5.73663535453863
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	5.73663535453863
96	6.6	0.22	0.58	1.1	0.133	52.0	136.0	0.9932	3.1	0.3	9.1	5	0	white	5.7988899403002
97	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	5.71251265081742
98	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	5.71251265081742
99	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	5.71251265081742
100	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	5.77266231423891

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.