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	3.9	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

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    | 424.62692 |24.61628|17.24984 | 0.00000
 fixed_acidity  |  0.40318  | 0.04264| 9.45456 | 0.00000
volatile_acidity| -1.28567  | 0.34387|-3.73879 | 0.00018
  citric_acid   |  0.15307  | 0.33713| 0.45402 | 0.64981
 residual_sugar |  0.12853  | 0.01377| 9.33334 | 0.00000
   chlorides    | -1.43865  | 1.85640|-0.77497 | 0.43836
    density     |-431.74147 |25.03718|-17.24401| 0.00000


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


===========
call_string
===========
logistic_reg('"public"."_verticapy_tmp_logisticregression_v_mldb_8697615e979911efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_86d6540e979911efa8720242ac120002_"', '"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|5197 

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	0.7390107248520705
prc_auc	0.40423073914659574
accuracy	0.81
log_loss	0.191289487675866
precision	0.6382978723404256
recall	0.11538461538461539
f1_score	0.19543973941368079
mcc	0.21221826742247502
informedness	0.09903846153846141
markedness	0.4547384150379514
csi	0.10830324909747292

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	0.7390107248520705
prc_auc	0.40423073914659574
accuracy	0.6753846153846154
log_loss	0.191289487675866
precision	0.3392857142857143
recall	0.6576923076923077
f1_score	0.4476439790575916
mcc	0.27708008915764676
informedness	0.3374999999999999
markedness	0.22747666905958353
csi	0.28836424957841483

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]: 0.81

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	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white	0
2	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
3	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	0
4	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
5	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	0
6	5.3	0.43	0.11	1.1	0.029	6.0	51.0	0.99076	3.51	0.48	11.2	4	0	white	0
7	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white	0
8	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
9	5.5	0.24	0.32	8.7	0.06	19.0	102.0	0.994	3.27	0.31	10.4	5	0	white	0
10	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white	0
11	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	0
12	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
13	5.6	0.225	0.24	9.8	0.054	59.0	140.0	0.99545	3.17	0.39	10.2	6	0	white	0
14	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	0
15	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0
16	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
17	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	0
18	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0
19	5.7	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	0
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	0
21	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
22	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	0
23	5.8	0.2	0.27	1.4	0.031	12.0	77.0	0.9905	3.25	0.36	10.9	7	1	white	0
24	5.8	0.25	0.26	13.1	0.051	44.0	148.0	0.9972	3.29	0.38	9.3	5	0	white	0
25	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
26	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
27	5.9	0.13	0.28	1.9	0.05	20.0	78.0	0.9918	3.43	0.64	10.8	6	0	white	0
28	5.9	0.17	0.28	0.7	0.027	5.0	28.0	0.98985	3.13	0.32	10.6	5	0	white	0
29	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	0
30	5.9	0.22	0.3	1.3	0.052	42.0	86.0	0.99069	3.31	0.47	11.55	6	0	white	0
31	5.9	0.23	0.28	8.6	0.046	37.0	142.0	0.99432	3.23	0.53	10.6	6	0	white	0
32	5.9	0.24	0.3	2.0	0.033	28.0	92.0	0.99225	3.39	0.69	10.9	7	1	white	0
33	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	0
34	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	0
35	5.9	0.26	0.27	18.2	0.048	52.0	168.0	0.9993	3.35	0.44	9.4	5	0	white	0
36	5.9	0.32	0.28	4.7	0.039	34.0	94.0	0.98964	3.22	0.57	13.1	7	1	white	0
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	0
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	0
39	6.0	0.16	0.22	1.6	0.042	36.0	106.0	0.9905	3.24	0.32	11.4	6	0	white	0
40	6.0	0.18	0.31	1.4	0.036	14.0	75.0	0.99085	3.34	0.58	11.1	8	1	white	0
41	6.0	0.19	0.26	12.4	0.048	50.0	147.0	0.9972	3.3	0.36	8.9	6	0	white	0
42	6.0	0.19	0.29	1.2	0.046	29.0	92.0	0.99033	3.22	0.53	11.3	6	0	white	0
43	6.0	0.2	0.32	3.0	0.031	26.0	118.0	0.99134	3.38	0.68	11.2	7	1	white	0
44	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	0
45	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	0
46	6.0	0.36	0.32	1.1	0.053	26.0	173.0	0.99414	3.38	0.54	8.8	5	0	white	0
47	6.0	0.52	0.33	7.7	0.046	24.0	119.0	0.99224	3.04	0.38	11.0	6	0	white	0
48	6.1	0.17	0.42	15.1	0.033	28.0	124.0	0.99684	2.87	0.47	9.5	5	0	white	0
49	6.1	0.17	0.42	15.1	0.033	28.0	124.0	0.99684	2.87	0.47	9.5	5	0	white	0
50	6.1	0.25	0.28	10.0	0.055	56.0	131.0	0.994	3.22	0.35	10.9	6	0	white	0
51	6.1	0.32	0.37	1.8	0.051	13.0	200.0	0.9945	3.49	0.44	10.5	4	0	white	0
52	6.1	0.34	0.21	5.0	0.042	17.0	133.0	0.99373	3.02	0.53	9.4	5	0	white	0
53	6.1	0.34	0.29	2.2	0.036	25.0	100.0	0.98938	3.06	0.44	11.8	6	0	white	0
54	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.2666666666667	7	1	white	0
55	6.1	0.46	0.32	6.2	0.053	10.0	94.0	0.99537	3.35	0.47	10.1	5	0	white	0
56	6.2	0.2	0.29	11.8	0.035	21.0	93.0	0.99364	3.18	0.34	11.9	6	0	white	0
57	6.2	0.21	0.28	5.7	0.028	45.0	121.0	0.99168	3.21	1.08	12.15	7	1	white	0
58	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	0
59	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	0
60	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	0
61	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	0
62	6.2	0.27	0.49	1.4	0.05	20.0	74.0	0.9931	3.32	0.44	9.8	6	0	white	0
63	6.2	0.28	0.41	5.0	0.043	50.0	188.0	0.99318	3.23	0.64	10.8	6	0	white	0
64	6.2	0.3	0.31	1.2	0.048	19.0	125.0	0.98999	3.32	0.54	12.6	6	0	white	0
65	6.2	0.3	0.32	1.7	0.032	30.0	130.0	0.9911	3.28	0.41	11.2	7	1	white	0
66	6.2	0.31	0.26	4.8	0.037	36.0	148.0	0.9928	3.21	0.41	10.4	6	0	white	0
67	6.2	0.34	0.25	12.1	0.059	33.0	171.0	0.99769	3.14	0.56	8.7	6	0	white	0
68	6.2	0.51	0.14	1.9	0.056	15.0	34.0	0.99396	3.48	0.57	11.5	6	0	red	0
69	6.3	0.17	0.42	2.8	0.028	45.0	107.0	0.9908	3.27	0.43	11.8	6	0	white	0
70	6.3	0.23	0.3	1.8	0.033	16.0	91.0	0.9906	3.28	0.4	11.8	6	0	white	0
71	6.3	0.23	0.33	6.9	0.052	23.0	118.0	0.9938	3.23	0.46	10.4	6	0	white	0
72	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white	0
73	6.3	0.255	0.37	1.1	0.04	37.0	114.0	0.9905	3.0	0.39	10.9	6	0	white	0
74	6.3	0.27	0.25	5.8	0.038	52.0	155.0	0.995	3.28	0.38	9.4	6	0	white	0
75	6.3	0.28	0.22	11.5	0.036	27.0	150.0	0.99445	3.0	0.33	10.6	6	0	white	0
76	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	0
77	6.3	0.48	0.04	1.1	0.046	30.0	99.0	0.9928	3.24	0.36	9.6	6	0	white	0
78	6.4	0.12	0.3	1.1	0.031	37.0	94.0	0.98986	3.01	0.56	11.7	6	0	white	0
79	6.4	0.14	0.31	1.2	0.034	53.0	138.0	0.99084	3.38	0.35	11.5	7	1	white	0
80	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	0
81	6.4	0.15	0.31	1.1	0.044	25.0	96.0	0.9932	3.54	0.51	10.3	6	0	white	0
82	6.4	0.17	0.27	1.5	0.037	20.0	98.0	0.9916	3.46	0.42	11.0	7	1	white	0
83	6.4	0.2	0.28	2.5	0.032	24.0	84.0	0.99168	3.31	0.55	11.5	5	0	white	0
84	6.4	0.2	0.32	3.1	0.041	18.0	126.0	0.9914	3.43	0.42	12.0	6	0	white	0
85	6.4	0.22	0.32	7.9	0.029	34.0	124.0	0.9948	3.4	0.39	10.2	5	0	white	0
86	6.4	0.25	0.32	0.9	0.034	40.0	114.0	0.99114	3.31	0.58	10.8	7	1	white	0
87	6.4	0.28	0.17	8.3	0.042	61.0	195.0	0.99577	3.22	0.46	9.4	5	0	white	0
88	6.4	0.29	0.28	11.1	0.063	66.0	169.0	0.9973	2.89	0.57	9.0	5	0	white	0
89	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	0
90	6.4	0.31	0.53	8.8	0.057	36.0	221.0	0.99642	3.17	0.44	9.1	5	0	white	0
91	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	0
92	6.4	0.4	0.25	4.2	0.032	15.0	91.0	0.98988	3.26	0.52	13.1	6	0	white	0
93	6.5	0.19	0.1	1.3	0.046	23.0	107.0	0.9937	3.29	0.45	10.0	5	0	white	0
94	6.5	0.21	0.35	5.7	0.043	47.0	197.0	0.99392	3.24	0.5	10.1	6	0	white	0
95	6.5	0.22	0.19	1.1	0.064	36.0	191.0	0.99297	3.05	0.5	9.5	6	0	white	0
96	6.5	0.22	0.29	7.4	0.028	16.0	87.0	0.99311	3.15	0.56	10.9	7	1	white	0
97	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0
98	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0
99	6.5	0.24	0.36	2.2	0.027	36.0	134.0	0.9898	3.28	0.36	12.5	7	1	white	0
100	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	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	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white	0	0.610843029562143	0.389156970437857
2	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	0.588327921519132	0.411672078480868
3	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	0	0.754901070652894	0.245098929347106
4	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	0.757251513593313	0.242748486406687
5	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	0	0.746790853371806	0.253209146628194
6	5.3	0.43	0.11	1.1	0.029	6.0	51.0	0.99076	3.51	0.48	11.2	4	0	white	0	0.806080155587417	0.193919844412583
7	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white	0	0.922004766402433	0.0779952335975673
8	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	0.898249560611269	0.101750439388731
9	5.5	0.24	0.32	8.7	0.06	19.0	102.0	0.994	3.27	0.31	10.4	5	0	white	0	0.82263104770791	0.17736895229209
10	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white	0	0.737356322342055	0.262643677657945
11	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	0	0.856372427957548	0.143627572042452
12	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	0.842069578037689	0.157930421962311
13	5.6	0.225	0.24	9.8	0.054	59.0	140.0	0.99545	3.17	0.39	10.2	6	0	white	0	0.876849217821667	0.123150782178333
14	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	0	0.672356654827902	0.327643345172098
15	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0	0.958479761487333	0.041520238512667
16	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	0.926946006223462	0.0730539937765379
17	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	0	0.922385331246538	0.0776146687534618
18	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0	0.882074325233106	0.117925674766894
19	5.7	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	0	0.547223706585135	0.452776293414865
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	0	0.805370297796337	0.194629702203663
21	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	0.850165620877032	0.149834379122968
22	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	0	0.911074789692008	0.088925210307992
23	5.8	0.2	0.27	1.4	0.031	12.0	77.0	0.9905	3.25	0.36	10.9	7	1	white	0	0.680271477708383	0.319728522291617
24	5.8	0.25	0.26	13.1	0.051	44.0	148.0	0.9972	3.29	0.38	9.3	5	0	white	0	0.903649120568949	0.0963508794310512
25	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	0.913474903480357	0.086525096519643
26	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	0.894565961455057	0.105434038544943
27	5.9	0.13	0.28	1.9	0.05	20.0	78.0	0.9918	3.43	0.64	10.8	6	0	white	0	0.759055160633415	0.240944839366585
28	5.9	0.17	0.28	0.7	0.027	5.0	28.0	0.98985	3.13	0.32	10.6	5	0	white	0	0.617319738880508	0.382680261119492
29	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	0	0.643685847819224	0.356314152180776
30	5.9	0.22	0.3	1.3	0.052	42.0	86.0	0.99069	3.31	0.47	11.55	6	0	white	0	0.702852607691912	0.297147392308088
31	5.9	0.23	0.28	8.6	0.046	37.0	142.0	0.99432	3.23	0.53	10.6	6	0	white	0	0.8171474567617	0.1828525432383
32	5.9	0.24	0.3	2.0	0.033	28.0	92.0	0.99225	3.39	0.69	10.9	7	1	white	0	0.808894350364421	0.191105649635579
33	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	0	0.911833758559136	0.0881662414408642
34	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	0	0.550326676384995	0.449673323615005
35	5.9	0.26	0.27	18.2	0.048	52.0	168.0	0.9993	3.35	0.44	9.4	5	0	white	0	0.921019359277652	0.0789806407223483
36	5.9	0.32	0.28	4.7	0.039	34.0	94.0	0.98964	3.22	0.57	13.1	7	1	white	0	0.520864271133583	0.479135728866417
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	0	0.842447666728618	0.157552333271382
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	0	0.853141723929472	0.146858276070529
39	6.0	0.16	0.22	1.6	0.042	36.0	106.0	0.9905	3.24	0.32	11.4	6	0	white	0	0.650384349056033	0.349615650943967
40	6.0	0.18	0.31	1.4	0.036	14.0	75.0	0.99085	3.34	0.58	11.1	8	1	white	0	0.690156422161441	0.309843577838559
41	6.0	0.19	0.26	12.4	0.048	50.0	147.0	0.9972	3.3	0.36	8.9	6	0	white	0	0.897184514632106	0.102815485367894
42	6.0	0.19	0.29	1.2	0.046	29.0	92.0	0.99033	3.22	0.53	11.3	6	0	white	0	0.65302350388038	0.34697649611962
43	6.0	0.2	0.32	3.0	0.031	26.0	118.0	0.99134	3.38	0.68	11.2	7	1	white	0	0.695029980660135	0.304970019339865
44	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	0	0.912890335262623	0.0871096647373769
45	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	0	0.878299856350013	0.121700143649987
46	6.0	0.36	0.32	1.1	0.053	26.0	173.0	0.99414	3.38	0.54	8.8	5	0	white	0	0.925130369863828	0.0748696301361725
47	6.0	0.52	0.33	7.7	0.046	24.0	119.0	0.99224	3.04	0.38	11.0	6	0	white	0	0.738786098237191	0.261213901762809
48	6.1	0.17	0.42	15.1	0.033	28.0	124.0	0.99684	2.87	0.47	9.5	5	0	white	0	0.825167725553841	0.174832274446159
49	6.1	0.17	0.42	15.1	0.033	28.0	124.0	0.99684	2.87	0.47	9.5	5	0	white	0	0.825167725553841	0.174832274446159
50	6.1	0.25	0.28	10.0	0.055	56.0	131.0	0.994	3.22	0.35	10.9	6	0	white	0	0.75713984023427	0.24286015976573
51	6.1	0.32	0.37	1.8	0.051	13.0	200.0	0.9945	3.49	0.44	10.5	4	0	white	0	0.922539737754322	0.077460262245678
52	6.1	0.34	0.21	5.0	0.042	17.0	133.0	0.99373	3.02	0.53	9.4	5	0	white	0	0.854566635451369	0.145433364548631
53	6.1	0.34	0.29	2.2	0.036	25.0	100.0	0.98938	3.06	0.44	11.8	6	0	white	0	0.557695586491768	0.442304413508232
54	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.2666666666667	7	1	white	0	0.740992249508082	0.259007750491918
55	6.1	0.46	0.32	6.2	0.053	10.0	94.0	0.99537	3.35	0.47	10.1	5	0	white	0	0.922582329729259	0.077417670270741
56	6.2	0.2	0.29	11.8	0.035	21.0	93.0	0.99364	3.18	0.34	11.9	6	0	white	0	0.64916522960566	0.35083477039434
57	6.2	0.21	0.28	5.7	0.028	45.0	121.0	0.99168	3.21	1.08	12.15	7	1	white	0	0.635879324177538	0.364120675822462
58	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	0	0.740033444315867	0.259966555684133
59	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	0	0.888338249229339	0.111661750770661
60	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	0	0.888338249229339	0.111661750770661
61	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	0	0.745049198146312	0.254950801853688
62	6.2	0.27	0.49	1.4	0.05	20.0	74.0	0.9931	3.32	0.44	9.8	6	0	white	0	0.858140731184744	0.141859268815256
63	6.2	0.28	0.41	5.0	0.043	50.0	188.0	0.99318	3.23	0.64	10.8	6	0	white	0	0.800078710224877	0.199921289775123
64	6.2	0.3	0.31	1.2	0.048	19.0	125.0	0.98999	3.32	0.54	12.6	6	0	white	0	0.63324598009792	0.36675401990208
65	6.2	0.3	0.32	1.7	0.032	30.0	130.0	0.9911	3.28	0.41	11.2	7	1	white	0	0.718409393680278	0.281590606319722
66	6.2	0.31	0.26	4.8	0.037	36.0	148.0	0.9928	3.21	0.41	10.4	6	0	white	0	0.786058564733589	0.213941435266411
67	6.2	0.34	0.25	12.1	0.059	33.0	171.0	0.99769	3.14	0.56	8.7	6	0	white	0	0.92730491658148	0.07269508341852
68	6.2	0.51	0.14	1.9	0.056	15.0	34.0	0.99396	3.48	0.57	11.5	6	0	red	0	0.922565704546107	0.0774342954538932
69	6.3	0.17	0.42	2.8	0.028	45.0	107.0	0.9908	3.27	0.43	11.8	6	0	white	0	0.607582449607078	0.392417550392922
70	6.3	0.23	0.3	1.8	0.033	16.0	91.0	0.9906	3.28	0.4	11.8	6	0	white	0	0.641538213394006	0.358461786605994
71	6.3	0.23	0.33	6.9	0.052	23.0	118.0	0.9938	3.23	0.46	10.4	6	0	white	0	0.790980567135358	0.209019432864642
72	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white	0	0.862834642937448	0.137165357062552
73	6.3	0.255	0.37	1.1	0.04	37.0	114.0	0.9905	3.0	0.39	10.9	6	0	white	0	0.659337420605905	0.340662579394095
74	6.3	0.27	0.25	5.8	0.038	52.0	155.0	0.995	3.28	0.38	9.4	6	0	white	0	0.884305803107216	0.115694196892785
75	6.3	0.28	0.22	11.5	0.036	27.0	150.0	0.99445	3.0	0.33	10.6	6	0	white	0	0.746177992988872	0.253822007011128
76	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	0	0.851634976874581	0.148365023125419
77	6.3	0.48	0.04	1.1	0.046	30.0	99.0	0.9928	3.24	0.36	9.6	6	0	white	0	0.880982455013083	0.119017544986917
78	6.4	0.12	0.3	1.1	0.031	37.0	94.0	0.98986	3.01	0.56	11.7	6	0	white	0	0.541879682776925	0.458120317223075
79	6.4	0.14	0.31	1.2	0.034	53.0	138.0	0.99084	3.38	0.35	11.5	7	1	white	0	0.647179479059887	0.352820520940113
80	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	0	0.713792892435205	0.286207107564794
81	6.4	0.15	0.31	1.1	0.044	25.0	96.0	0.9932	3.54	0.51	10.3	6	0	white	0	0.840997724339871	0.159002275660129
82	6.4	0.17	0.27	1.5	0.037	20.0	98.0	0.9916	3.46	0.42	11.0	7	1	white	0	0.720156193162195	0.279843806837805
83	6.4	0.2	0.28	2.5	0.032	24.0	84.0	0.99168	3.31	0.55	11.5	5	0	white	0	0.707047937129679	0.292952062870321
84	6.4	0.2	0.32	3.1	0.041	18.0	126.0	0.9914	3.43	0.42	12.0	6	0	white	0	0.665946377066309	0.334053622933691
85	6.4	0.22	0.32	7.9	0.029	34.0	124.0	0.9948	3.4	0.39	10.2	5	0	white	0	0.82481583301856	0.17518416698144
86	6.4	0.25	0.32	0.9	0.034	40.0	114.0	0.99114	3.31	0.58	10.8	7	1	white	0	0.713948525678691	0.286051474321309
87	6.4	0.28	0.17	8.3	0.042	61.0	195.0	0.99577	3.22	0.46	9.4	5	0	white	0	0.884474335071518	0.115525664928482
88	6.4	0.29	0.28	11.1	0.063	66.0	169.0	0.9973	2.89	0.57	9.0	5	0	white	0	0.913915241563829	0.0860847584361713
89	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	0	0.924019937188985	0.0759800628110152
90	6.4	0.31	0.53	8.8	0.057	36.0	221.0	0.99642	3.17	0.44	9.1	5	0	white	0	0.905245156373213	0.0947548436267866
91	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	0	0.664566296896265	0.335433703103735
92	6.4	0.4	0.25	4.2	0.032	15.0	91.0	0.98988	3.26	0.52	13.1	6	0	white	0	0.536727487616394	0.463272512383606
93	6.5	0.19	0.1	1.3	0.046	23.0	107.0	0.9937	3.29	0.45	10.0	5	0	white	0	0.870113697906581	0.129886302093419
94	6.5	0.21	0.35	5.7	0.043	47.0	197.0	0.99392	3.24	0.5	10.1	6	0	white	0	0.804480376008828	0.195519623991172
95	6.5	0.22	0.19	1.1	0.064	36.0	191.0	0.99297	3.05	0.5	9.5	6	0	white	0	0.840665688826738	0.159334311173262
96	6.5	0.22	0.29	7.4	0.028	16.0	87.0	0.99311	3.15	0.56	10.9	7	1	white	0	0.699891422664126	0.300108577335874
97	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0	0.914193360295178	0.085806639704822
98	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0	0.914193360295178	0.085806639704822
99	6.5	0.24	0.36	2.2	0.027	36.0	134.0	0.9898	3.28	0.36	12.5	7	1	white	0	0.524890807456669	0.475109192543331
100	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	0	0.782871074864352	0.217128925135648

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([[1023,   17],
       [ 230,   30]])

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 /notebooks/ml/model_tracking_versioning/index.ipynb 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(- (424.62692415138 + 0.403179029947125 * "fixed_acidity" + -1.28566720131892 * "volatile_acidity" + 0.153065045355106 * "citric_acid" + 0.128532468331258 * "residual_sugar" + -1.4386487597294 * "chlorides" + -431.741467616055 * "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`