Feedback

×

• Feedback
• Was this page helpful?*
  • Yes
  • No
• Do you have any additional feedback?
  • Yes
  • No
• Additional Feedback

verticapy.machine_learning.vertica.linear_model.PoissonRegressor.predict

PoissonRegressor.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    | 150.88602 | 6.89376|21.88734 | 0.00000
 fixed_acidity  |  0.14831  | 0.01244|11.91894 | 0.00000
volatile_acidity| -0.71179  | 0.08791|-8.09727 | 0.00000
  citric_acid   | -0.09086  | 0.09599|-0.94660 | 0.34389
 residual_sugar |  0.04657  | 0.00387|12.04549 | 0.00000
   chlorides    | -0.02024  | 0.38848|-0.05211 | 0.95844
    density     |-146.89734 | 7.01153|-20.95083| 0.00000


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


===========
call_string
===========
linear_reg('"public"."_verticapy_tmp_linearregression_v_mldb_fcccdbc4979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_fd222476979911efa8720242ac120002_"', '"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	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	6.13234762827753
2	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	5.83534371057723
3	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	5.74587997679663
4	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	5.68159288597147
5	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	6.31985583869766
6	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	5.7134924380112
7	5.2	0.335	0.2	1.7	0.033	17.0	74.0	0.99002	3.34	0.48	12.3	6	0	white	6.04780609604683
8	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	6.06465161577049
9	5.4	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white	6.45202534176269
10	5.6	0.12	0.33	2.9	0.044	21.0	73.0	0.98896	3.17	0.32	12.9	8	1	white	6.45972214086351
11	5.6	0.12	0.33	2.9	0.044	21.0	73.0	0.98896	3.17	0.32	12.9	8	1	white	6.45972214086351
12	5.6	0.25	0.19	2.4	0.049	42.0	166.0	0.992	3.25	0.43	10.4	6	0	white	5.90995748620654
13	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	5.97247400116262
14	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	6.19452710834176
15	5.6	0.615	0.0	1.6	0.089	16.0	59.0	0.9943	3.58	0.52	9.9	5	0	red	5.2914901244184
16	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	5.18084058804806
17	5.7	0.15	0.28	3.7	0.045	57.0	151.0	0.9913	3.22	0.27	11.2	6	0	white	6.15123594044513
18	5.7	0.21	0.32	1.6	0.03	33.0	122.0	0.99044	3.33	0.52	11.9	6	0	white	6.13373913955135
19	5.7	0.26	0.3	1.8	0.039	30.0	105.0	0.98995	3.48	0.52	12.5	7	1	white	6.18107759434662
20	5.7	0.28	0.36	1.8	0.041	38.0	90.0	0.99002	3.27	0.98	11.9	7	1	white	6.15106661275428
21	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	6.15524720236164
22	5.8	0.31	0.32	4.5	0.024	28.0	94.0	0.98906	3.25	0.52	13.7	7	1	white	6.4152740757892
23	5.8	0.61	0.11	1.8	0.066	18.0	28.0	0.99483	3.55	0.66	10.9	6	0	red	5.24663959313975
24	5.9	0.12	0.27	4.8	0.03	40.0	110.0	0.99226	3.55	0.68	12.1	6	0	white	6.11366608370852
25	5.9	0.14	0.2	1.6	0.04	26.0	114.0	0.99105	3.25	0.45	11.4	6	0	white	6.13432062355452
26	5.9	0.21	0.31	1.8	0.033	45.0	142.0	0.98984	3.35	0.5	12.7	6	0	white	6.26170092770718
27	6.0	0.16	0.27	12.0	0.03	39.0	98.0	0.99402	3.15	0.34	10.8	5	0	white	6.17676625051752
28	6.0	0.2	0.25	2.0	0.041	30.0	95.0	0.99078	3.27	0.56	11.1	6	0	white	6.16016963937335
29	6.0	0.22	0.28	1.1	0.034	47.0	90.0	0.98862	3.22	0.38	12.6	6	0	white	6.41873780532561
30	6.0	0.26	0.33	4.35	0.04	15.0	80.0	0.98934	3.29	0.5	12.7	6	0	white	6.43117707931336
31	6.0	0.27	0.26	1.3	0.038	32.0	138.0	0.99125	3.46	0.43	11.1	6	0	white	6.00785779149135
32	6.0	0.29	0.25	1.4	0.033	30.0	114.0	0.98794	3.08	0.43	13.2	6	0	white	6.48551863623754
33	6.0	0.32	0.3	1.3	0.025	18.0	112.0	0.98802	3.07	0.64	13.3	7	1	white	6.44337515752005
34	6.0	0.32	0.3	7.3	0.174	46.0	159.0	0.99519	3.18	0.49	9.4	5	0	white	5.66650502296534
35	6.0	0.35	0.46	0.9	0.033	9.0	65.0	0.98934	3.24	0.35	12.1	4	0	white	6.19479007360204
36	6.0	0.5	0.0	1.4	0.057	15.0	26.0	0.99448	3.36	0.45	9.5	5	0	red	5.39756356726804
37	6.1	0.2	0.25	1.2	0.038	34.0	128.0	0.9921	3.24	0.44	10.1	5	0	white	5.943903612463
38	6.1	0.21	0.38	1.5	0.039	37.0	122.0	0.98972	3.2	0.43	12.0	6	0	white	6.28853882921791
39	6.1	0.22	0.5	6.6	0.045	30.0	122.0	0.99415	3.22	0.49	9.9	6	0	white	5.85713069097309
40	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white	5.67010937482917
41	6.1	0.31	0.34	2.8	0.042	59.5	162.0	0.99179	3.27	0.47	10.8	6	0	white	5.97739253897873
42	6.1	0.32	0.24	1.5	0.036	38.0	124.0	0.9898	3.29	0.42	12.4	7	1	white	6.21127144436412
43	6.1	0.32	0.33	10.7	0.036	27.0	98.0	0.99521	3.34	0.52	10.2	6	0	white	5.83679262732932
44	6.1	0.4	0.18	9.0	0.051	28.5	259.0	0.9964	3.19	0.5	8.8	5	0	white	5.5392038743085
45	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white	6.13403060379792
46	6.2	0.25	0.31	3.2	0.03	32.0	150.0	0.99014	3.18	0.31	12.0	6	0	white	6.29890731109523
47	6.2	0.25	0.54	7.0	0.046	58.0	176.0	0.99454	3.19	0.7	10.4	5	0	white	5.80828989408397
48	6.2	0.26	0.2	8.0	0.047	35.0	111.0	0.99445	3.11	0.42	10.4	6	0	white	5.891832802333
49	6.2	0.28	0.43	13.0	0.039	64.0	233.0	0.99745	3.08	0.38	8.9	5	0	white	5.6490016693952
50	6.2	0.32	0.16	7.0	0.045	30.0	136.0	0.9949	3.18	0.47	9.6	6	0	white	5.7401297648351
51	6.2	0.32	0.45	2.9	0.029	37.0	94.0	0.98998	3.25	0.6	12.4	6	0	white	6.24591469744877
52	6.2	0.36	0.14	8.9	0.036	38.0	155.0	0.99622	3.27	0.5	9.4	5	0	white	5.60822972668427
53	6.2	0.36	0.14	8.9	0.036	38.0	155.0	0.99622	3.27	0.5	9.4	5	0	white	5.60822972668427
54	6.3	0.13	0.42	1.1	0.043	63.0	146.0	0.99066	3.13	0.72	11.2	7	1	white	6.2147187301899
55	6.3	0.2	0.18	10.6	0.045	57.0	159.0	0.99666	3.09	0.54	9.2	5	0	white	5.74765948818134
56	6.3	0.22	0.34	5.0	0.032	36.0	93.0	0.99012	3.27	0.36	13.5	7	1	white	6.41908375810681
57	6.3	0.26	0.25	5.2	0.046	11.0	133.0	0.99202	2.97	0.68	11.0	6	0	white	6.12871474617245
58	6.3	0.26	0.25	7.8	0.058	44.0	166.0	0.9961	3.24	0.41	9.0	5	0	white	5.65020406841933
59	6.3	0.32	0.17	17.75	0.06	51.0	190.0	0.99916	3.13	0.48	8.8	6	0	white	5.62855775039526
60	6.3	0.32	0.32	1.5	0.03	24.0	101.0	0.98923	3.21	0.42	13.0	5	0	white	6.31751744299444
61	6.3	0.34	0.36	4.9	0.035	31.0	185.0	0.9946	3.15	0.49	9.7	5	0	white	5.66903384951843
62	6.3	0.39	0.08	1.7	0.066	3.0	20.0	0.9954	3.34	0.58	9.4	5	0	red	5.3917271940347
63	6.3	0.4	0.24	5.1	0.036	43.0	131.0	0.99186	3.24	0.44	11.3	6	0	white	6.04902169485305
64	6.4	0.105	0.29	1.1	0.035	44.0	140.0	0.99142	3.17	0.55	10.7	7	1	white	6.14767687927267
65	6.4	0.125	0.29	5.85	0.042	24.0	99.0	0.992	3.23	0.32	12.0	7	1	white	6.26929060710222
66	6.4	0.15	0.44	1.2	0.043	67.0	150.0	0.9907	3.14	0.73	11.2	7	1	white	6.21227744312867
67	6.4	0.17	0.32	2.4	0.048	41.0	200.0	0.9938	3.5	0.5	9.7	6	0	white	5.80934227885953
68	6.4	0.22	0.32	7.2	0.028	15.0	83.0	0.993	3.13	0.55	10.9	8	1	white	6.11519545763232
69	6.4	0.24	0.25	20.2	0.083	35.0	157.0	0.99976	3.17	0.5	9.1	5	0	white	5.71854754415105
70	6.4	0.24	0.32	0.95	0.041	23.0	131.0	0.99033	3.25	0.35	11.8	5	0	white	6.20187053329872
71	6.4	0.24	0.49	5.8	0.053	25.0	120.0	0.9942	3.01	0.98	10.5	6	0	white	5.84353651733758
72	6.4	0.24	0.49	5.8	0.053	25.0	120.0	0.9942	3.01	0.98	10.5	6	0	white	5.84353651733758
73	6.4	0.24	0.5	11.6	0.047	60.0	211.0	0.9966	3.18	0.57	9.3	5	0	white	5.76028250875322
74	6.4	0.25	0.41	8.6	0.042	57.0	173.0	0.9965	3.0	0.44	9.1	5	0	white	5.63643320502462
75	6.4	0.25	0.57	1.0	0.062	21.0	122.0	0.99238	3.0	0.4	9.5	5	0	white	5.87280036528665
76	6.4	0.27	0.49	7.3	0.046	53.0	206.0	0.9956	3.24	0.43	9.2	6	0	white	5.6865181886767
77	6.4	0.29	0.18	15.0	0.04	21.0	116.0	0.99736	3.14	0.5	9.2	5	0	white	5.80059567946628
78	6.4	0.31	0.31	12.9	0.045	55.0	161.0	0.99546	3.02	0.59	10.2	5	0	white	5.95576122540115
79	6.4	0.33	0.28	1.1	0.038	30.0	110.0	0.9917	3.12	0.42	10.5	6	0	white	5.94724008530568
80	6.4	0.43	0.27	1.1	0.054	5.0	110.0	0.9939	3.24	0.52	9.1	4	0	white	5.55347137127376
81	6.4	0.5	0.2	2.4	0.059	19.0	112.0	0.99314	3.18	0.4	9.2	6	0	white	5.68208375584143
82	6.4	0.57	0.02	1.8	0.067	4.0	11.0	0.997	3.46	0.68	9.5	5	0	red	5.05348801638158
83	6.5	0.13	0.37	1.0	0.036	48.0	114.0	0.9911	3.41	0.51	11.5	8	1	white	6.17977426838314
84	6.5	0.16	0.33	4.8	0.043	45.0	114.0	0.992	3.18	0.44	11.2	6	0	white	6.20665911313964
85	6.5	0.18	0.33	1.4	0.029	35.0	138.0	0.99114	3.36	0.6	11.5	7	1	white	6.16071164750679
86	6.5	0.19	0.32	1.4	0.04	31.0	132.0	0.9922	3.36	0.54	10.8	7	1	white	5.99856849387365
87	6.5	0.22	0.31	3.9	0.046	17.0	106.0	0.99098	3.15	0.31	11.5	5	0	white	6.27363333555761
88	6.5	0.22	0.5	16.4	0.048	36.0	182.0	0.99904	3.02	0.49	8.8	6	0	white	5.65441978538234
89	6.5	0.25	0.45	7.8	0.048	52.0	188.0	0.99576	3.2	0.53	9.1	5	0	white	5.71895894929449
90	6.5	0.26	0.31	3.6	0.03	36.0	92.0	0.99026	3.22	0.62	12.6	8	1	white	6.3372815912264
91	6.5	0.37	0.3	2.2	0.033	39.0	107.0	0.98894	3.22	0.53	13.5	7	1	white	6.38854338136832
92	6.5	0.37	0.33	3.5	0.036	23.0	92.0	0.99136	3.18	0.38	11.2	6	0	white	6.09080187731689
93	6.5	0.41	0.24	14.0	0.048	24.0	113.0	0.9982	3.44	0.53	9.8	6	0	white	5.55443736489377
94	6.5	0.46	0.31	5.0	0.027	15.0	72.0	0.99165	3.26	0.6	11.5	7	1	white	6.05598976701989
95	6.5	0.48	0.02	0.9	0.043	32.0	99.0	0.99226	3.14	0.47	9.8	4	0	white	5.78724967697934
96	6.6	0.16	0.34	1.1	0.037	41.0	115.0	0.9899	3.01	0.68	12.0	6	0	white	6.35689068521921
97	6.6	0.16	0.57	1.1	0.13	58.0	140.0	0.9927	3.12	0.39	9.3	7	1	white	5.92279682285789
98	6.6	0.17	0.3	1.1	0.031	13.0	73.0	0.99095	3.17	0.58	11.0	6	0	white	6.19928656074731
99	6.6	0.19	0.35	1.5	0.037	37.0	107.0	0.99006	3.18	0.68	12.0	7	1	white	6.32975135627632
100	6.6	0.21	0.29	5.35	0.029	43.0	106.0	0.99112	2.93	0.43	11.5	7	1	white	6.34469982393324

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.