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verticapy.machine_learning.vertica.ensemble.RandomForestRegressor.predict

RandomForestRegressor.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	3.8	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,
)



=======
details
=======
   predictor    |coefficient|std_err | t_value |p_value 
----------------+-----------+--------+---------+--------
   Intercept    | 155.60915 | 6.84239|22.74192 | 0.00000
 fixed_acidity  |  0.15068  | 0.01238|12.17306 | 0.00000
volatile_acidity| -0.69937  | 0.08838|-7.91304 | 0.00000
  citric_acid   | -0.09591  | 0.09443|-1.01572 | 0.30981
 residual_sugar |  0.04322  | 0.00382|11.32451 | 0.00000
   chlorides    |  0.04147  | 0.37962| 0.10925 | 0.91301
    density     |-151.65007 | 6.95932|-21.79094| 0.00000


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


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_demo_da39166e55a311ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_da48ceec55a311ef880f0242ac120002_"', '"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|5200 

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	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white	5.11478769213235
2	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	6.31711947785311
3	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	5.98285820965313
4	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	5.7474574802917
5	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	6.3458351901412
6	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	5.68409606866857
7	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	5.79192076670216
8	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	5.99904133842318
9	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	5.90603852094378
10	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	5.43386766691555
11	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	6.24172623500067
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	6.19462859809417
13	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	5.8402522422021
14	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	5.62671609568807
15	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	6.0954210160119
16	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	5.94270348880801
17	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	5.98453904340857
18	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	5.98453904340857
19	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	5.65257965488857
20	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	5.82252340131078
21	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	6.22927761702064
22	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	5.78286908283127
23	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	6.30087718069828
24	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red	5.85858159549917
25	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	6.13984435130834
26	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	5.99141653827411
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	5.58154046551391
28	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	5.4698880316451
29	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	5.50571123532217
30	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	6.47994707441322
31	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	5.98922337091537
32	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	6.19169991660931
33	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	6.000254203135
34	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	5.98789890960219
35	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	5.89139367626393
36	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	5.76120956673361
37	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	6.44739020447952
38	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	5.64970978649009
39	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	6.11629195131138
40	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	6.23982200625463
41	5.4	0.59	0.07	7.0	0.045	36.0	147.0	0.9944	3.34	0.57	9.7	6	0	white	5.50706007010209
42	5.4	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red	5.43404940084812
43	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	6.11235848322542
44	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	5.7907978484393
45	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	6.24598527907335
46	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	5.92183232603446
47	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	5.90522336173808
48	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	5.87768043970047
49	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	5.82901913399164
50	5.5	0.28	0.21	1.6	0.032	23.0	85.0	0.99027	3.42	0.42	12.5	5	0	white	6.11791198635026
51	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	6.31987009029041
52	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	6.19633607147532
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	6.08889350489406
54	5.5	0.62	0.33	1.7	0.037	24.0	118.0	0.98758	3.15	0.39	13.55	6	0	white	6.28108352980328
55	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	5.92199778251825
56	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	6.06660785837619
57	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	6.03572173055582
58	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	6.40612905871146
59	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white	6.07569429367578
60	5.6	0.18	0.58	1.25	0.034	29.0	129.0	0.98984	3.51	0.6	12.0	7	1	white	6.21759607043046
61	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	6.05479754051606
62	5.6	0.23	0.25	8.0	0.043	31.0	101.0	0.99429	3.19	0.42	10.4	6	0	white	5.83152141289679
63	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	5.78399388734417
64	5.6	0.31	0.78	13.9	0.074	23.0	92.0	0.99677	3.39	0.48	10.5	6	0	red	5.6049084382978
65	5.6	0.33	0.28	1.2	0.031	33.0	97.0	0.99126	3.49	0.58	10.9	6	0	white	5.92383616197247
66	5.6	0.34	0.25	2.5	0.046	47.0	182.0	0.99093	3.21	0.4	11.3	5	0	white	6.0265680168566
67	5.6	0.34	0.3	6.9	0.038	23.0	89.0	0.99266	3.25	0.49	11.1	6	0	white	5.94923881228527
68	5.6	0.35	0.37	1.0	0.038	6.0	72.0	0.9902	3.37	0.34	11.4	5	0	white	6.05361252166156
69	5.6	0.35	0.4	6.3	0.022	23.0	174.0	0.9922	3.54	0.5	11.6	7	1	white	5.97581920839815
70	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	6.13718185460976
71	5.6	0.42	0.34	2.4	0.022	34.0	97.0	0.98915	3.22	0.38	12.8	7	1	white	6.22660610369266
72	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	6.05419425268576
73	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	6.05419425268576
74	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	5.37649103503932
75	5.6	0.62	0.03	1.5	0.08	6.0	13.0	0.99498	3.66	0.62	10.1	4	0	red	5.19585571485584
76	5.7	0.135	0.3	4.6	0.042	19.0	101.0	0.9946	3.31	0.42	9.3	6	0	white	5.71424514609234
77	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	5.54750848685322
78	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	5.58429647844497
79	5.7	0.24	0.3	1.3	0.03	25.0	98.0	0.98968	3.37	0.43	12.4	7	1	white	6.24381706587715
80	5.7	0.24	0.47	6.3	0.069	35.0	182.0	0.99391	3.11	0.46	9.75	5	0	white	5.80373217460817
81	5.7	0.25	0.21	1.5	0.044	21.0	108.0	0.99142	3.3	0.59	11.0	6	0	white	5.99080843388191
82	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	6.00911469325482
83	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	5.68337335239664
84	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	5.7290775176883
85	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	5.96855094598399
86	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	6.3356218740125
87	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	6.17523463747466
88	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	6.12923767250786
89	5.7	0.335	0.34	1.0	0.04	13.0	174.0	0.992	3.27	0.66	10.0	5	0	white	5.80916177476897
90	5.7	0.4	0.35	5.1	0.026	17.0	113.0	0.99052	3.18	0.67	12.4	6	0	white	6.16379285836243
91	5.7	0.4	0.35	5.1	0.026	17.0	113.0	0.99052	3.18	0.67	12.4	6	0	white	6.16379285836243
92	5.7	1.13	0.09	1.5	0.172	7.0	19.0	0.994	3.5	0.48	9.8	4	0	red	5.00092216709507
93	5.8	0.12	0.21	1.3	0.056	35.0	121.0	0.9908	3.32	0.33	11.4	6	0	white	6.18267261221524
94	5.8	0.18	0.28	1.3	0.034	9.0	94.0	0.99092	3.21	0.52	11.2	6	0	white	6.11488589828352
95	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	6.46445857599824
96	5.8	0.19	0.24	1.3	0.044	38.0	128.0	0.99362	3.77	0.6	10.6	5	0	white	5.70268829477021
97	5.8	0.19	0.33	4.2	0.038	49.0	133.0	0.99107	3.16	0.42	11.3	7	1	white	6.20584290115616
98	5.8	0.24	0.26	10.05	0.039	63.0	162.0	0.99375	3.33	0.5	11.2	6	0	white	6.02402450487671
99	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	6.09481938691061
100	5.8	0.25	0.28	11.1	0.056	45.0	175.0	0.99755	3.42	0.43	9.5	5	0	white	5.48492469053153

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.