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verticapy.machine_learning.vertica.linear_model.LogisticRegression.predict_proba

LogisticRegression.predict_proba(vdf: Annotated[str | vDataFrame, ''], X: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, name: str | None = None, pos_label: Annotated[bool | float | str | timedelta | datetime, 'Python Scalar'] | None = None, inplace: bool = True) → vDataFrame

Returns the model’s probabilities 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 vDataColumn. If empty, a name is generated.

pos_label: PythonScalar, optional

Class label. For binary classification, this can be either 1 or 0.

inplace: bool, optional

If set to True, the prediction is added to the vDataFrame.

Returns

vDataFrame

the input object.

Examples

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

train, test = data.train_test_split(test_size = 0.5)

Let’s import the model:

from verticapy.machine_learning.vertica import LogisticRegression

Then we can create the model:

model = LogisticRegression(
    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",
    ],
    "good",
    test,
)



=======
details
=======
   predictor    |coefficient|std_err | z_value |p_value 
----------------+-----------+--------+---------+--------
   Intercept    | 424.11398 |31.58406|13.42810 | 0.00000
 fixed_acidity  |  0.40629  | 0.05625| 7.22279 | 0.00000
volatile_acidity| -1.05805  | 0.42134|-2.51117 | 0.01203
  citric_acid   |  0.45964  | 0.41899| 1.09701 | 0.27264
 residual_sugar |  0.12977  | 0.01745| 7.43605 | 0.00000
   chlorides    | -2.92208  | 2.40054|-1.21726 | 0.22350
    density     |-431.31122 |32.12773|-13.42489| 0.00000


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


===========
call_string
===========
logistic_reg('"public"."_verticapy_tmp_logisticregression_v_mldb_afb251e8979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_b006a932979911efa8720242ac120002_"', '"good"', '"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  |  5  
rejected_row_count|  0  
accepted_row_count|3246 

model.predict_proba(
    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_0 Float(22)	123 prediction_1 Float(22)
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	0.614223143928793	0.385776856071207
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	0.892402055440255	0.107597944559745
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	0.534028038523843	0.465971961476157
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	0.80013517923838	0.19986482076162
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	0.853981579092806	0.146018420907194
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	0.853981579092806	0.146018420907194
7	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	0.901124203304446	0.0988757966955535
8	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	0.67011836406655	0.32988163593345
9	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	0.935001171637576	0.0649988283624239
10	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	0.665970992897736	0.334029007102264
11	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	0.851900625670385	0.148099374329615
12	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	0.652355339600388	0.347644660399612
13	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	0.674287614454658	0.325712385545342
14	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	0.831256377269966	0.168743622730034
15	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	0.850986453305688	0.149013546694312
16	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	0.570334762703147	0.429665237296853
17	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	0.701307140844782	0.298692859155218
18	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	0.924693100924146	0.0753068990758541
19	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	0.9149458010886	0.0850541989114003
20	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	0.699175093186306	0.300824906813694
21	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	0.793424192360719	0.206575807639281
22	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	0.77434226291243	0.22565773708757
23	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	0.741554553096482	0.258445446903518
24	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	0.741554553096482	0.258445446903518
25	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	0.860234920250667	0.139765079749333
26	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	0.967055192275571	0.0329448077244286
27	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	0.822462019027975	0.177537980972025
28	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	0.78808383867199	0.21191616132801
29	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	0.906764402232677	0.0932355977673228
30	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	0.6912693384504	0.3087306615496
31	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	0.828714913206066	0.171285086793934
32	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	0.846878072420145	0.153121927579855
33	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	0.821432720121621	0.178567279878379
34	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	0.861202916908724	0.138797083091276
35	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	0.77251929306607	0.22748070693393
36	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	0.710842993165706	0.289157006834294
37	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	0.710842993165706	0.289157006834294
38	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	0.750370715595331	0.249629284404669
39	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	0.837858089686119	0.162141910313881
40	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	0.829945666515662	0.170054333484338
41	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	0.922673007695568	0.0773269923044318
42	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	0.922673007695568	0.0773269923044318
43	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	0.922673007695568	0.0773269923044318
44	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	0.58080979752002	0.41919020247998
45	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	0.641514437056429	0.358485562943571
46	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	0.90155845835812	0.0984415416418801
47	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	0.90155845835812	0.0984415416418801
48	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	0.751357702074879	0.248642297925121
49	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	0.873199009943253	0.126800990056747
50	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	0.873199009943253	0.126800990056747
51	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	0.895854510662489	0.104145489337511
52	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	0.728285273503613	0.271714726496387
53	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	0.655542426427927	0.344457573572073
54	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	0.817466113030251	0.182533886969749
55	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	0.746973054088569	0.253026945911431
56	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	0.645674139401791	0.354325860598209
57	5.8	0.18	0.37	1.1	0.036	31.0	96.0	0.98942	3.16	0.48	12.0	6	0	white	0.564707792167871	0.435292207832129
58	5.8	0.22	0.29	0.9	0.034	34.0	89.0	0.98936	3.14	0.36	11.1	7	1	white	0.582638794656089	0.417361205343911
59	5.8	0.22	0.29	1.3	0.036	25.0	68.0	0.98865	3.24	0.35	12.6	6	0	white	0.495330175369026	0.504669824630974
60	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0.731036610845946	0.268963389154054
61	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0.731036610845946	0.268963389154054
62	5.8	0.28	0.18	1.2	0.058	7.0	108.0	0.99288	3.23	0.58	9.55	4	0	white	0.880489404181561	0.119510595818439
63	5.8	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white	0.757924248044949	0.242075751955051
64	5.8	0.29	0.33	3.7	0.029	30.0	88.0	0.98994	3.25	0.42	12.3	6	0	white	0.565001179802294	0.434998820197706
65	5.8	0.32	0.28	4.3	0.032	46.0	115.0	0.98946	3.16	0.57	13.0	8	1	white	0.510019206507991	0.489980793492009
66	5.8	0.32	0.28	4.3	0.032	46.0	115.0	0.98946	3.16	0.57	13.0	8	1	white	0.510019206507991	0.489980793492009
67	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	0.646310971199862	0.353689028800138
68	5.8	0.385	0.25	3.7	0.031	38.0	122.0	0.99128	3.2	0.63	11.2	6	0	white	0.727618188484407	0.272381811515593
69	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	0.642526071387124	0.357473928612876
70	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	0.814813274380502	0.185186725619498
71	5.9	0.15	0.31	5.8	0.041	53.0	155.0	0.9945	3.52	0.46	10.5	6	0	white	0.859512351760395	0.140487648239605
72	5.9	0.17	0.3	1.4	0.042	25.0	119.0	0.9931	3.68	0.72	10.5	6	0	white	0.859008851996731	0.140991148003269
73	5.9	0.19	0.21	1.7	0.045	57.0	135.0	0.99341	3.32	0.44	9.5	5	0	red	0.877949269704416	0.122050730295584
74	5.9	0.19	0.21	1.7	0.045	57.0	135.0	0.99341	3.32	0.44	9.5	5	0	white	0.877949269704416	0.122050730295584
75	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	0.589921697392621	0.410078302607379
76	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	0.828135833238401	0.171864166761599
77	5.9	0.23	0.24	1.6	0.037	32.0	115.0	0.99076	3.21	0.51	11.4	6	0	white	0.700212638533863	0.299787361466137
78	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	0.697930504478935	0.302069495521065
79	5.9	0.26	0.24	2.4	0.046	27.0	132.0	0.99234	3.63	0.73	11.3	5	0	white	0.815176866661921	0.184823133338079
80	5.9	0.27	0.27	5.0	0.035	14.0	97.0	0.99058	3.1	0.33	11.8	7	1	white	0.587136835476205	0.412863164523795
81	5.9	0.29	0.33	7.4	0.037	58.0	205.0	0.99495	3.26	0.41	9.6	5	0	white	0.872689184046311	0.127310815953689
82	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	0.555619016590006	0.444380983409994
83	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	0.909233272235222	0.0907667277647784
84	5.9	0.44	0.0	1.6	0.042	3.0	11.0	0.9944	3.48	0.85	11.7	6	0	red	0.940777096862314	0.0592229031376858
85	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	0.726306937118801	0.273693062881199
86	6.0	0.16	0.3	6.7	0.043	43.0	153.0	0.9951	3.63	0.46	10.6	5	0	white	0.873650667235649	0.126349332764351
87	6.0	0.17	0.29	9.7	0.044	33.0	98.0	0.99536	3.12	0.36	9.2	6	0	white	0.842182407124397	0.157817592875603
88	6.0	0.19	0.29	1.1	0.047	67.0	152.0	0.9916	3.54	0.59	11.1	7	1	white	0.768308576804712	0.231691423195288
89	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	0.732408573853511	0.267591426146489
90	6.0	0.21	0.38	0.8	0.02	22.0	98.0	0.98941	3.26	0.32	11.8	6	0	white	0.548358880463757	0.451641119536243
91	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	0.839685784054374	0.160314215945626
92	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	0.836506054132271	0.163493945867729
93	6.0	0.26	0.32	3.8	0.029	48.0	180.0	0.99011	3.15	0.34	12.0	6	0	white	0.553154968685271	0.446845031314729
94	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	0.597792101061081	0.402207898938919
95	6.0	0.27	0.3	14.7	0.044	15.0	144.0	0.99666	3.12	0.53	10.3	6	0	white	0.843910209621116	0.156089790378884
96	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	0.61585888836925	0.38414111163075
97	6.0	0.28	0.35	1.9	0.037	16.0	120.0	0.9933	3.16	0.69	10.6	5	0	white	0.866067009584378	0.133932990415622
98	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	0.852079344054554	0.147920655945446
99	6.0	0.38	0.26	3.5	0.035	38.0	111.0	0.98872	3.18	0.47	13.6	7	1	white	0.456339485538703	0.543660514461297
100	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	0.933142375613654	0.0668576243863464

Rows: 1-100 | Columns: 16

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.