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verticapy.machine_learning.vertica.neighbors.KernelDensity.predict

KernelDensity.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    | 147.96958 | 6.90075|21.44254 | 0.00000
 fixed_acidity  |  0.13966  | 0.01235|11.30735 | 0.00000
volatile_acidity| -0.73953  | 0.08821|-8.38390 | 0.00000
  citric_acid   | -0.18589  | 0.09424|-1.97259 | 0.04860
 residual_sugar |  0.04465  | 0.00386|11.57838 | 0.00000
   chlorides    | -0.14789  | 0.39129|-0.37796 | 0.70547
    density     |-143.84513 | 7.01850|-20.49513| 0.00000


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


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_mldb_0805330a979b11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_084795a6979b11efa8720242ac120002_"', '"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|5203 

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	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white	5.99948797141519
2	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	6.21847881560126
3	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	6.20568064244034
4	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	5.65456458400391
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	5.99617552502613
6	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	5.838040864167
7	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6	0	white	5.83145648817242
8	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	5.84104322788335
9	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	5.50553931644524
10	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	6.0311119227004
11	5.3	0.585	0.07	7.1	0.044	34.0	145.0	0.9945	3.34	0.57	9.7	6	0	white	5.52067882397802
12	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	5.38936206746712
13	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	6.23227671447282
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	6.1172817882092
15	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	6.14229135608301
16	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	6.33597513415077
17	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	5.96494383157082
18	5.6	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	5.49587712092705
19	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	5.66246641724103
20	5.7	0.31	0.29	7.3	0.05	33.0	143.0	0.99332	3.31	0.5	11.0666666666667	6	0	white	5.91679745451427
21	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	6.24267792283393
22	5.7	0.385	0.04	12.6	0.034	22.0	115.0	0.9964	3.28	0.63	9.9	6	0	white	5.70379841263946
23	5.7	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	5.52935130987382
24	5.8	0.2	0.3	1.5	0.031	21.0	57.0	0.99115	3.44	0.55	11.0	6	0	white	6.06620965609778
25	5.8	0.21	0.32	1.6	0.045	38.0	95.0	0.98946	3.23	0.94	12.4	8	1	white	6.3005897162426
26	5.8	0.24	0.39	1.5	0.054	37.0	158.0	0.9932	3.21	0.52	9.3	6	0	white	5.72161397506747
27	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	5.5373294479329
28	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	5.66287606728093
29	5.8	0.28	0.27	2.6	0.054	30.0	156.0	0.9914	3.53	0.42	12.4	5	0	white	6.02238101849466
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	6.04185528254678
31	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	5.73465103594032
32	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	5.69427584355003
33	5.9	0.2	0.23	1.5	0.037	38.0	93.0	0.99021	3.36	0.49	12.0	6	0	white	6.22751471110544
34	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	5.66336865009134
35	5.9	0.25	0.27	1.5	0.029	37.0	81.0	0.9892	3.2	0.46	12.2	6	0	white	6.32956903505507
36	5.9	0.27	0.27	9.0	0.051	43.0	136.0	0.9941	3.25	0.53	10.7	6	0	white	5.94159358268075
37	5.9	0.36	0.04	5.7	0.046	21.0	87.0	0.9934	3.22	0.51	10.2	5	0	white	5.87186134666095
38	5.9	0.415	0.13	1.4	0.04	11.0	64.0	0.9922	3.29	0.52	10.5	5	0	white	5.79594313118093
39	5.9	0.65	0.23	5.0	0.035	20.0	128.0	0.99016	3.46	0.48	12.8	6	0	white	6.05850370739313
40	6.0	0.24	0.41	1.3	0.036	42.0	118.0	0.99018	3.04	0.64	11.7333333333333	6	0	white	6.17397062484679
41	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	5.64820583660983
42	6.0	0.33	0.26	5.1	0.051	16.0	119.0	0.99416	3.15	0.41	9.2	5	0	white	5.73026209933312
43	6.0	0.33	0.26	5.1	0.051	16.0	119.0	0.99416	3.15	0.41	9.2	5	0	white	5.73026209933312
44	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	5.5935389783854
45	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	6.05716625358966
46	6.0	0.42	0.19	2.0	0.075	22.0	47.0	0.99522	3.39	0.78	10.0	6	0	red	5.38226177007482
47	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	5.57124793474205
48	6.1	0.18	0.38	2.3	0.033	28.0	111.0	0.98962	3.16	0.49	12.4	6	0	white	6.36353658924236
49	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	6.23854135084653
50	6.1	0.25	0.18	10.5	0.049	41.0	124.0	0.9963	3.14	0.35	10.5	5	0	white	5.75186413609885
51	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	5.80141623797874
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	5.51740473165196
53	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	6.13959624419334
54	6.1	0.38	0.14	3.9	0.06	27.0	113.0	0.99344	3.07	0.34	9.2	4	0	white	5.77820985022944
55	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	5.36827454088856
56	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	5.97234337412766
57	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	5.39676634457021
58	6.2	0.19	0.29	4.3	0.045	33.0	126.0	0.99658	3.18	0.42	9.3	6	0	white	5.47320961399666
59	6.2	0.2	0.33	5.4	0.028	21.0	75.0	0.99012	3.36	0.41	13.5	7	1	white	6.43925240646348
60	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	5.66451278604936
61	6.2	0.25	0.44	15.8	0.057	39.0	167.0	0.99804	3.14	0.51	9.2	5	0	white	5.70269379696859
62	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	5.63534837022647
63	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	6.47312778431368
64	6.2	0.37	0.3	6.6	0.346	79.0	200.0	0.9954	3.29	0.58	9.6	5	0	white	5.56616202491094
65	6.2	0.46	0.29	2.1	0.074	32.0	98.0	0.99578	3.33	0.62	9.8	5	0	red	5.28608236610339
66	6.2	0.58	0.0	1.6	0.065	8.0	18.0	0.9966	3.56	0.84	9.4	5	0	red	5.11229766392779
67	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	5.8920575638202
68	6.3	0.24	0.55	8.1	0.04	67.0	216.0	0.99596	3.24	0.5	9.2	5	0	white	5.66147766833771
69	6.3	0.28	0.29	6.8	0.051	40.0	143.0	0.99374	3.43	0.59	11.0	6	0	white	5.93988655131733
70	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	5.82160489146352
71	6.3	0.33	0.2	17.9	0.066	36.0	161.0	0.9991	3.14	0.51	8.8	5	0	white	5.64207863743061
72	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	5.80346705034245
73	6.3	0.41	0.16	0.9	0.032	25.0	98.0	0.99274	3.16	0.42	9.5	5	0	white	5.75110564139794
74	6.3	0.68	0.01	3.7	0.103	32.0	54.0	0.99586	3.51	0.66	11.3	6	0	red	5.2450511612569
75	6.4	0.13	0.47	1.6	0.092	40.0	158.0	0.9928	3.21	0.36	9.8	6	0	white	5.92826823524092
76	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	6.12695132212812
77	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	5.77887038633153
78	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	5.81287499740606
79	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	6.41847600404947
80	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	5.68747334484638
81	6.4	0.31	0.09	1.4	0.066	15.0	28.0	0.99459	3.42	0.7	10.0	7	1	red	5.60322256150008
82	6.4	0.31	0.28	1.5	0.037	12.0	119.0	0.9919	3.32	0.51	10.4	7	1	white	5.96360079330651
83	6.4	0.38	0.26	8.2	0.043	28.0	98.0	0.99234	2.99	0.31	11.4	6	0	white	6.15055828427035
84	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	5.71625840791697
85	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	6.0810895728238
86	6.5	0.17	0.54	8.5	0.082	64.0	163.0	0.9959	2.89	0.39	8.8	6	0	white	5.76331617124816
87	6.5	0.24	0.39	17.3	0.052	22.0	126.0	0.99888	3.11	0.47	9.2	6	0	white	5.70817191644264
88	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	6.00014311644688
89	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	5.79677860017847
90	6.5	0.28	0.34	3.6	0.04	29.0	121.0	0.99111	3.28	0.48	12.1	7	1	white	6.19556760315498
91	6.5	0.32	0.12	11.5	0.033	35.0	165.0	0.9974	3.22	0.32	9.0	5	0	white	5.65590373317167
92	6.5	0.32	0.23	1.2	0.054	39.0	208.0	0.99272	3.18	0.46	9.9	6	0	white	5.84560202962354
93	6.5	0.43	0.31	3.6	0.046	19.0	143.0	0.99022	3.15	0.34	12.0	8	1	white	6.21734905428767
94	6.5	0.52	0.11	1.8	0.073	13.0	38.0	0.9955	3.34	0.52	9.3	5	0	red	5.34409564538797
95	6.5	0.58	0.0	2.2	0.096	3.0	13.0	0.99557	3.62	0.62	11.5	4	0	red	5.3245629156354
96	6.6	0.16	0.4	1.5	0.044	48.0	143.0	0.9912	3.54	0.52	12.4	7	1	white	6.17981129416142
97	6.6	0.17	0.35	2.6	0.03	33.0	78.0	0.99146	3.22	0.72	11.3	6	0	white	6.19550144225792
98	6.6	0.24	0.27	10.3	0.047	54.0	219.0	0.99742	3.04	0.45	8.8	5	0	white	5.6426152068903
99	6.6	0.26	0.22	18.15	0.05	23.0	139.0	0.99904	3.06	0.5	9.2	5	0	white	5.75418545712085
100	6.6	0.26	0.25	11.6	0.045	45.0	178.0	0.99691	3.33	0.43	9.8	6	0	white	5.76325022757339

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.