verticapy.machine_learning.vertica.linear_model.LogisticRegression¶

class verticapy.machine_learning.vertica.linear_model.LogisticRegression(name: str = None, overwrite_model: bool = False, penalty: Literal['none', 'l1', 'l2', 'enet', None] = 'none', tol: float = 1e-06, C: Annotated[int | float | Decimal, 'Python Numbers'] = 1.0, max_iter: int = 100, solver: Literal['newton', 'bfgs', 'cgd'] = 'newton', l1_ratio: float = 0.5, fit_intercept: bool = True)¶

Creates a LogisticRegression object using the Vertica Logistic Regression algorithm.

Parameters¶

name: str, optional

Name of the model. The model is stored in the database.

overwrite_model: bool, optional

If set to True, training a model with the same name as an existing model overwrites the existing model.

penalty: str, optional

Determines the method of regularization.

None:
No Regularization.

l1:
L1 Regularization.

l2:
L2 Regularization.

enet:
Combination between L1 and L2.

tol: float, optional

Determines whether the algorithm has reached the specified accuracy result.

C: PythonNumber, optional

The regularization parameter value. The value must be zero or non-negative.

max_iter: int, optional

Determines the maximum number of iterations the algorithm performs before achieving the specified accuracy result.

solver: str, optional

The optimizer method used to train the model.

newton:
Newton Method.

bfgs:
Broyden Fletcher Goldfarb Shanno.

cgd:
Coordinate Gradient Descent.

l1_ratio: float, optional

ENet mixture parameter that defines the provided ratio of L1 versus L2 regularization.

fit_intercept: bool, optional

boolean, specifies whether the model includes an intercept. If set to False, no intercept is used in training the model. Note that setting fit_intercept to False does not work well with the BFGS optimizer.

Attributes¶

Many attributes are created during the fitting phase.

coef_: numpy.array: The regression coefficients. The order of coefficients is the same as the order of columns used during the fitting phase.
intercept_: float: The expected value of the dependent variable when all independent variables are zero, serving as the baseline or constant term in the model.
features_importance_: numpy.array: The importance of features is computed through the model coefficients, which are normalized based on their range. Subsequently, an activation function calculates the final score. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.
classes_: numpy.array: The classes labels.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_vertica_attributes() method.

Examples¶

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Load data for machine learning¶

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

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

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

You can easily divide your dataset into training and testing subsets using the vDataFrame.train_test_split() method. This is a crucial step when preparing your data for machine learning, as it allows you to evaluate the performance of your models accurately.

data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, VerticaPy utilizes seeded randomization to guarantee the reproducibility of your data split. However, please be aware that this approach may lead to reduced performance. For a more efficient data split, you can use the vDataFrame.to_db() method to save your results into tables or temporary tables. This will help enhance the overall performance of the process.

Balancing the Dataset¶

In VerticaPy, balancing a dataset to address class imbalances is made straightforward through the balance() function within the preprocessing module. This function enables users to rectify skewed class distributions efficiently. By specifying the target variable and setting parameters like the method for balancing, users can effortlessly achieve a more equitable representation of classes in their dataset. Whether opting for over-sampling, under-sampling, or a combination of both, VerticaPy’s balance() function streamlines the process, empowering users to enhance the performance and fairness of their machine learning models trained on imbalanced data.

To balance the dataset, use the following syntax.

from verticapy.machine_learning.vertica.preprocessing import balance

balanced_train = balance(
    name = "my_schema.train_balanced",
    input_relation = train,
    y = "good",
    method = "hybrid",
)

Note

With this code, a table named train_balanced is created in the my_schema schema. It can then be used to train the model. In the rest of the example, we will work with the full dataset.

Hint

Balancing the dataset is a crucial step in improving the accuracy of machine learning models, particularly when faced with imbalanced class distributions. By addressing disparities in the number of instances across different classes, the model becomes more adept at learning patterns from all classes rather than being biased towards the majority class. This, in turn, enhances the model’s ability to make accurate predictions for under-represented classes. The balanced dataset ensures that the model is not dominated by the majority class and, as a result, leads to more robust and unbiased model performance. Therefore, by employing techniques such as over-sampling, under-sampling, or a combination of both during dataset preparation, practitioners can significantly contribute to achieving higher accuracy and better generalization of their machine learning models.

Model Initialization¶

First we import the LogisticRegression 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,
)

Hint

In verticapy 1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.

Important

The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Training¶

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    | 415.73513 |24.79194|16.76897 | 0.00000
 fixed_acidity  |  0.42795  | 0.04321| 9.90463 | 0.00000
volatile_acidity| -1.02378  | 0.33343|-3.07048 | 0.00214
  citric_acid   |  0.11464  | 0.33826| 0.33892 | 0.73467
 residual_sugar |  0.12371  | 0.01389| 8.90360 | 0.00000
   chlorides    | -3.53379  | 2.04925|-1.72443 | 0.08463
    density     |-422.89180 |25.22383|-16.76557| 0.00000


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


===========
call_string
===========
logistic_reg('"public"."_verticapy_tmp_logisticregression_v_demo_1da9869055a411ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_1dbab15455a411ef880f0242ac120002_"', '"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|5194 

Important

To train a model, you can directly use the vDataFrame or the name of the relation stored in the database. The test set is optional and is only used to compute the test metrics. In verticapy, we don’t work using X matrices and y vectors. Instead, we work directly with lists of predictors and the response name.

Features Importance¶

We can conveniently get the features importance:

result = model.features_importance()

Note

For LinearModel, feature importance is computed using the coefficients. These coefficients are then normalized using the feature distribution. An activation function is applied to get the final score.

Metrics¶

We can get the entire report using:

model.report()

	value
auc	0.7483550960764561
prc_auc	0.36365007758621154
accuracy	0.8127398311588642
log_loss	0.183137254525043
precision	0.46938775510204084
recall	0.0954356846473029
f1_score	0.15862068965517243
mcc	0.14480990087742882
informedness	0.07095357541943104
markedness	0.2955440549425512
csi	0.08614232209737828

Rows: 1-11 | Columns: 2

Important

Most metrics are computed using a single SQL query, but some of them might require multiple SQL queries. Selecting only the necessary metrics in the report can help optimize performance. E.g. model.report(metrics = ["auc", "accuracy"]).

For classification models, we can easily modify the cutoff to observe the effect on different metrics:

model.report(cutoff = 0.2)

	value
auc	0.7483550960764561
prc_auc	0.36365007758621154
accuracy	0.6968534151957022
log_loss	0.183137254525043
precision	0.3389121338912134
recall	0.6721991701244814
f1_score	0.4506258692628651
mcc	0.3018231574854275
informedness	0.3746473810472686
markedness	0.24315455813363762
csi	0.29084380610412924

Rows: 1-11 | Columns: 2

You can also use the LinearModel.score function to compute any classification metric. The default metric is the accuracy:

model.score()
Out[2]: 0.8127398311588642

Prediction¶

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 Integer
1	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	0
2	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	0
3	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	0
4	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	0
5	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	0
6	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	0
7	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	0
8	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	0
9	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	0
10	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	0
11	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	0
12	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	0
13	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	0
14	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
15	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	0
16	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	0
17	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
18	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	0
19	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	0
20	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	0
21	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	0
22	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	0
23	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
24	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	0
25	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	0
26	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	0
27	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	0
28	5.3	0.275	0.24	7.4	0.038	28.0	114.0	0.99313	3.38	0.51	11.0	6	0	white	0
29	5.3	0.3	0.2	1.1	0.077	48.0	166.0	0.9944	3.3	0.54	8.7	4	0	white	0
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	1
31	5.3	0.6	0.34	1.4	0.031	3.0	60.0	0.98854	3.27	0.38	13.0	6	0	white	0
32	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	0
33	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white	0
34	5.4	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white	0
35	5.4	0.29	0.47	3.0	0.052	47.0	145.0	0.993	3.29	0.75	10.0	6	0	white	0
36	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	0
37	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
38	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	0
39	5.5	0.315	0.38	2.6	0.033	10.0	69.0	0.9909	3.12	0.59	10.8	6	0	white	0
40	5.5	0.32	0.13	1.3	0.037	45.0	156.0	0.99184	3.26	0.38	10.7	5	0	white	0
41	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	0
42	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
43	5.5	0.485	0.0	1.5	0.065	8.0	103.0	0.994	3.63	0.4	9.7	4	0	white	0
44	5.6	0.12	0.26	4.3	0.038	18.0	97.0	0.99477	3.36	0.46	9.2	5	0	white	0
45	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	1
46	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white	0
47	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	0
48	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	0
49	5.6	0.19	0.31	2.7	0.027	11.0	100.0	0.98964	3.46	0.4	13.2	7	1	white	0
50	5.6	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	0
51	5.6	0.255	0.57	10.7	0.056	66.0	171.0	0.99464	3.25	0.61	10.4	7	1	white	0
52	5.6	0.28	0.4	6.1	0.034	36.0	118.0	0.99144	3.21	0.43	12.1	7	1	white	0
53	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	0
54	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	0
55	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	0
56	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	0
57	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	0
58	5.7	0.12	0.26	5.5	0.034	21.0	99.0	0.99324	3.09	0.57	9.9	6	0	white	0
59	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	0
60	5.7	0.21	0.24	2.3	0.047	60.0	189.0	0.995	3.65	0.72	10.1	6	0	white	0
61	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
62	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	0
63	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	0
64	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	0
65	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	0
66	5.7	0.22	0.33	1.9	0.036	37.0	110.0	0.98945	3.26	0.58	12.4	6	0	white	0
67	5.7	0.23	0.25	7.95	0.042	16.0	108.0	0.99486	3.44	0.61	10.3	6	0	white	0
68	5.7	0.25	0.22	9.8	0.049	50.0	125.0	0.99571	3.2	0.45	10.1	6	0	white	0
69	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
70	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
71	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	0
72	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	0
73	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	0
74	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	0
75	5.7	0.32	0.5	2.6	0.049	17.0	155.0	0.9927	3.22	0.64	10.0	6	0	white	0
76	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	0
77	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	0
78	5.7	0.6	0.0	1.4	0.063	11.0	18.0	0.99191	3.45	0.56	12.2	6	0	red	0
79	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	0
80	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	0
81	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
82	5.8	0.15	0.28	0.8	0.037	43.0	127.0	0.99198	3.24	0.51	9.3	5	0	white	0
83	5.8	0.23	0.21	1.5	0.044	21.0	110.0	0.99138	3.3	0.57	11.0	6	0	white	0
84	5.8	0.23	0.27	1.8	0.043	24.0	69.0	0.9933	3.38	0.31	9.4	6	0	white	0
85	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	0
86	5.8	0.26	0.3	2.6	0.034	75.0	129.0	0.9902	3.2	0.38	11.5	4	0	white	0
87	5.8	0.27	0.27	12.3	0.045	55.0	170.0	0.9972	3.28	0.42	9.3	6	0	white	0
88	5.8	0.28	0.34	2.2	0.037	24.0	125.0	0.98986	3.36	0.33	12.8	8	1	white	0
89	5.8	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	0
90	5.8	0.29	0.27	1.6	0.062	17.0	140.0	0.99138	3.23	0.35	11.1	6	0	white	0
91	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0
92	5.8	0.31	0.33	1.2	0.036	23.0	99.0	0.9916	3.18	0.6	10.5	6	0	white	0
93	5.8	0.315	0.19	19.4	0.031	28.0	106.0	0.99704	2.97	0.4	10.55	6	0	white	0
94	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
95	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
96	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	0
97	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	0
98	5.8	0.35	0.29	3.2	0.034	41.0	151.0	0.9912	3.35	0.58	11.6333333333333	7	1	white	0
99	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	0
100	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

Rows: 1-100 | Columns: 15

Note

Predictions can be made automatically using the test set, in which case you don’t need to specify the predictors. Alternatively, you can pass only the vDataFrame to the predict() function, but in this case, it’s essential that the column names of the vDataFrame match the predictors and response name in the model.

Probabilities¶

It is also easy to get the model’s probabilities:

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 Integer	123 prediction_0 Float(22)	123 prediction_1 Float(22)
1	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	0	0.929269244575015	0.0707307554249849
2	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	0	0.801328461787073	0.198671538212927
3	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	0	0.941851920225126	0.0581480797748741
4	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	0	0.746677701219933	0.253322298780067
5	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	0	0.698914799279339	0.301085200720661
6	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	0	0.878306255064043	0.121693744935957
7	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	0	0.882947690912749	0.117052309087251
8	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	0	0.873657603334266	0.126342396665734
9	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	0	0.784101931347202	0.215898068652798
10	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	0	0.863924107480709	0.136075892519291
11	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	0	0.598221950603486	0.401778049396514
12	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	0	0.598221950603486	0.401778049396514
13	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	0	0.880942767228735	0.119057232771265
14	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	0.897317199190153	0.102682800809847
15	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	0	0.85984718803412	0.14015281196588
16	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	0	0.817993983931696	0.182006016068304
17	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	0.934947684453691	0.0650523155463092
18	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	0	0.847431675959903	0.152568324040097
19	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	0	0.669192477151522	0.330807522848478
20	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	0	0.759089533391291	0.240910466608709
21	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	0	0.649891214630244	0.350108785369756
22	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	0	0.649891214630244	0.350108785369756
23	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	0.837567084589387	0.162432915410613
24	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	0	0.972162938007159	0.0278370619928409
25	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	0	0.843055991033716	0.156944008966284
26	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	0	0.921210624334456	0.0787893756655442
27	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	0	0.870163589656482	0.129836410343518
28	5.3	0.275	0.24	7.4	0.038	28.0	114.0	0.99313	3.38	0.51	11.0	6	0	white	0	0.810941354491315	0.189058645508685
29	5.3	0.3	0.2	1.1	0.077	48.0	166.0	0.9944	3.3	0.54	8.7	4	0	white	0	0.949816148104999	0.0501838518950014
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	1	0.449011182603007	0.550988817396993
31	5.3	0.6	0.34	1.4	0.031	3.0	60.0	0.98854	3.27	0.38	13.0	6	0	white	0	0.63502758391609	0.36497241608391
32	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	0	0.931211557408965	0.0687884425910352
33	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white	0	0.900835808878449	0.0991641911215511
34	5.4	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white	0	0.873027967042641	0.126972032957359
35	5.4	0.29	0.47	3.0	0.052	47.0	145.0	0.993	3.29	0.75	10.0	6	0	white	0	0.874478287035938	0.125521712964062
36	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	0	0.669052383327578	0.330947616672422
37	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	0.908212165087678	0.0917878349123216
38	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	0	0.636718316224455	0.363281683775545
39	5.5	0.315	0.38	2.6	0.033	10.0	69.0	0.9909	3.12	0.59	10.8	6	0	white	0	0.736631782773328	0.263368217226672
40	5.5	0.32	0.13	1.3	0.037	45.0	156.0	0.99184	3.26	0.38	10.7	5	0	white	0	0.836824722596075	0.163175277403925
41	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	0	0.781288142054281	0.218711857945719
42	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	0.853329411086519	0.146670588913481
43	5.5	0.485	0.0	1.5	0.065	8.0	103.0	0.994	3.63	0.4	9.7	4	0	white	0	0.943013589281072	0.0569864107189279
44	5.6	0.12	0.26	4.3	0.038	18.0	97.0	0.99477	3.36	0.46	9.2	5	0	white	0	0.904115694828874	0.0958843051711257
45	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	1	0.493296360631617	0.506703639368383
46	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white	0	0.801485133724335	0.198514866275665
47	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	0	0.698510961672366	0.301489038327634
48	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	0	0.697362384164152	0.302637615835848
49	5.6	0.19	0.31	2.7	0.027	11.0	100.0	0.98964	3.46	0.4	13.2	7	1	white	0	0.574289769119056	0.425710230880944
50	5.6	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	0	0.850121909333156	0.149878090666844
51	5.6	0.255	0.57	10.7	0.056	66.0	171.0	0.99464	3.25	0.61	10.4	7	1	white	0	0.826838053645214	0.173161946354786
52	5.6	0.28	0.4	6.1	0.034	36.0	118.0	0.99144	3.21	0.43	12.1	7	1	white	0	0.678421959674295	0.321578040325705
53	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	0	0.896980316822253	0.103019683177747
54	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	0	0.790993949143885	0.209006050856115
55	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	0	0.878917809240191	0.121082190759808
56	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	0	0.923164209712602	0.0768357902873977
57	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	0	0.956722156500512	0.0432778434994884
58	5.7	0.12	0.26	5.5	0.034	21.0	99.0	0.99324	3.09	0.57	9.9	6	0	white	0	0.800817227403573	0.199182772596428
59	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	0	0.815644319754428	0.184355680245572
60	5.7	0.21	0.24	2.3	0.047	60.0	189.0	0.995	3.65	0.72	10.1	6	0	white	0	0.935350464972676	0.0646495350273244
61	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	0.92499399303271	0.0750060069672896
62	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	0	0.916820667176913	0.0831793328230867
63	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	0	0.916820667176913	0.0831793328230867
64	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	0	0.916820667176913	0.0831793328230867
65	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	0	0.916820667176913	0.0831793328230867
66	5.7	0.22	0.33	1.9	0.036	37.0	110.0	0.98945	3.26	0.58	12.4	6	0	white	0	0.583078214817348	0.416921785182652
67	5.7	0.23	0.25	7.95	0.042	16.0	108.0	0.99486	3.44	0.61	10.3	6	0	white	0	0.871629131056802	0.128370868943198
68	5.7	0.25	0.22	9.8	0.049	50.0	125.0	0.99571	3.2	0.45	10.1	6	0	white	0	0.890385862642385	0.109614137357615
69	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	0.90581370684138	0.0941862931586196
70	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	0.763184835940917	0.236815164059083
71	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	0	0.937197803444106	0.0628021965558945
72	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	0	0.937197803444106	0.0628021965558945
73	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	0	0.780008779146712	0.219991220853288
74	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	0	0.81002691458417	0.18997308541583
75	5.7	0.32	0.5	2.6	0.049	17.0	155.0	0.9927	3.22	0.64	10.0	6	0	white	0	0.852211551755482	0.147788448244518
76	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	0	0.665154941740562	0.334845058259438
77	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	0	0.631509581340628	0.368490418659372
78	5.7	0.6	0.0	1.4	0.063	11.0	18.0	0.99191	3.45	0.56	12.2	6	0	red	0	0.876519151111356	0.123480848888644
79	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	0	0.976996010235943	0.0230039897640575
80	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	0	0.888673118580693	0.111326881419307
81	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	0.817043699182619	0.182956300817381
82	5.8	0.15	0.28	0.8	0.037	43.0	127.0	0.99198	3.24	0.51	9.3	5	0	white	0	0.807869406807493	0.192130593192507
83	5.8	0.23	0.21	1.5	0.044	21.0	110.0	0.99138	3.3	0.57	11.0	6	0	white	0	0.770397641691531	0.229602358308469
84	5.8	0.23	0.27	1.8	0.043	24.0	69.0	0.9933	3.38	0.31	9.4	6	0	white	0	0.87814555349597	0.12185444650403
85	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	0	0.702696552529802	0.297303447470198
86	5.8	0.26	0.3	2.6	0.034	75.0	129.0	0.9902	3.2	0.38	11.5	4	0	white	0	0.636573646057505	0.363426353942495
87	5.8	0.27	0.27	12.3	0.045	55.0	170.0	0.9972	3.28	0.42	9.3	6	0	white	0	0.914771513000854	0.0852284869991464
88	5.8	0.28	0.34	2.2	0.037	24.0	125.0	0.98986	3.36	0.33	12.8	8	1	white	0	0.620745824262878	0.379254175737122
89	5.8	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	0	0.856567767286086	0.143432232713914
90	5.8	0.29	0.27	1.6	0.062	17.0	140.0	0.99138	3.23	0.35	11.1	6	0	white	0	0.788572717752791	0.211427282247209
91	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0	0.842774713496546	0.157225286503454
92	5.8	0.31	0.33	1.2	0.036	23.0	99.0	0.9916	3.18	0.6	10.5	6	0	white	0	0.799084420954684	0.200915579045316
93	5.8	0.315	0.19	19.4	0.031	28.0	106.0	0.99704	2.97	0.4	10.55	6	0	white	0	0.807385632934514	0.192614367065486
94	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	0.664092235032478	0.335907764967522
95	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	0.664092235032478	0.335907764967522
96	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	0	0.89614499182522	0.10385500817478
97	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	0	0.839066908948819	0.160933091051181
98	5.8	0.35	0.29	3.2	0.034	41.0	151.0	0.9912	3.35	0.58	11.6333333333333	7	1	white	0	0.731545245113022	0.268454754886978
99	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	0	0.72624002926937	0.27375997073063
100	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	0.731897338913294	0.268102661086706

Rows: 1-100 | Columns: 17

Note

Probabilities are added to the vDataFrame, and VerticaPy uses the corresponding probability function in SQL behind the scenes. You can use the pos_label parameter to add only the probability of the selected category.

Confusion Matrix¶

You can obtain the confusion matrix of your choice by specifying the desired cutoff.

model.confusion_matrix(cutoff = 0.5)
Out[3]: 
array([[1036,   26],
       [ 218,   23]])

Note

In classification, the cutoff is a threshold value used to determine class assignment based on predicted probabilities or scores from a classification model. In binary classification, if the predicted probability for a specific class is greater than or equal to the cutoff, the instance is assigned to the positive class; otherwise, it is assigned to the negative class. Adjusting the cutoff allows for trade-offs between true positives and false positives, enabling the model to be optimized for specific objectives or to consider the relative costs of different classification errors. The choice of cutoff is critical for tailoring the model’s performance to meet specific needs.

Main Plots (Classification Curves)¶

Classification models allow for the creation of various plots that are very helpful in understanding the model, such as the ROC Curve, PRC Curve, Cutoff Curve, Gain Curve, and more.

Most of the classification curves can be found in the Machine Learning - Classification Curve.

For example, let’s draw the model’s ROC curve.

model.roc_curve()

Important

Most of the curves have a parameter called nbins, which is essential for estimating metrics. The larger the nbins, the more precise the estimation, but it can significantly impact performance. Exercise caution when increasing this parameter excessively.

Hint

In binary classification, various curves can be easily plotted. However, in multi-class classification, it’s important to select the pos_label, representing the class to be treated as positive when drawing the curve.

Other Plots¶

If the model allows, you can also generate relevant plots. For example, classification plots can be found in the Machine Learning - Classification Plots.

model.plot()

Important

The plotting feature is typically suitable for models with fewer than three predictors.

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour()

Machine learning models with two predictors can usually benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios. Please refer to Contour Plot for more examples.

Parameter Modification¶

In order to see the parameters:

model.get_params()
Out[4]: 
{'penalty': 'none',
 'tol': 1e-06,
 'max_iter': 100,
 'solver': 'newton',
 'fit_intercept': True}

And to manually change some of the parameters:

model.set_params({'tol': 0.001})

Model Register¶

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to Model Tracking and Versioning for more details on model tracking and versioning.

Model Exporting¶

To Memmodel

model.to_memmodel()

Note

MemModel objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle a scikit-learn model.

The following methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

To SQL

You can get the SQL code by:

model.to_sql()
Out[6]: '((1 / (1 + EXP(- (415.735132735572 + 0.427948559732272 * "fixed_acidity" + -1.02378159951502 * "volatile_acidity" + 0.114642962693097 * "citric_acid" + 0.12370889208529 * "residual_sugar" + -3.53378957112363 * "chlorides" + -422.891801425139 * "density")))) > 0.5)::int'

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]

model.to_python()(X)
Out[8]: array([0])

Hint

The to_python() method is used to retrieve predictions, probabilities, or cluster distances. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

__init__(name: str = None, overwrite_model: bool = False, penalty: Literal['none', 'l1', 'l2', 'enet', None] = 'none', tol: float = 1e-06, C: Annotated[int | float | Decimal, 'Python Numbers'] = 1.0, max_iter: int = 100, solver: Literal['newton', 'bfgs', 'cgd'] = 'newton', l1_ratio: float = 0.5, fit_intercept: bool = True) → None¶

Methods

`__init__`([name, overwrite_model, penalty, ...])
`classification_report`([metrics, cutoff, nbins])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`confusion_matrix`([cutoff])	Computes the model confusion matrix.
`contour`([nbins, chart])	Draws the model's contour plot.
`cutoff_curve`([nbins, show, chart])	Draws the model Cutoff curve.
`deploySQL`([X, cutoff])	Returns the SQL code needed to deploy the model.
`does_model_exists`(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
`drop`()	Drops the model from the Vertica database.
`export_models`(name, path[, kind])	Exports machine learning models.
`features_importance`([show, chart])	Computes the model's features importance.
`fit`(input_relation, X, y[, test_relation, ...])	Trains the model.
`get_attributes`([attr_name])	Returns the model attributes.
`get_match_index`(x, col_list[, str_check])	Returns the matching index.
`get_params`()	Returns the parameters of the model.
`get_plotting_lib`([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
`get_vertica_attributes`([attr_name])	Returns the model Vertica attributes.
`import_models`(path[, schema, kind])	Imports machine learning models.
`lift_chart`([nbins, show, chart])	Draws the model Lift Chart.
`plot`([max_nb_points, chart])	Draws the model.
`prc_curve`([nbins, show, chart])	Draws the model PRC curve.
`predict`(vdf[, X, name, cutoff, inplace])	Makes predictions on the input relation.
`predict_proba`(vdf[, X, name, pos_label, inplace])	Returns the model's probabilities using the input relation.
`register`(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
`report`([metrics, cutoff, nbins])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`roc_curve`([nbins, show, chart])	Draws the model ROC curve.
`score`([metric, cutoff, nbins])	Computes the model score.
`set_params`([parameters])	Sets the parameters of the model.
`summarize`()	Summarizes the model.
`to_binary`(path)	Exports the model to the Vertica Binary format.
`to_memmodel`()	Converts the model to an InMemory object that can be used for different types of predictions.
`to_pmml`(path)	Exports the model to PMML.
`to_python`([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
`to_sql`([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
`to_tf`(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes

`classes_`
`object_type`