verticapy.machine_learning.vertica.svm.LinearSVC¶

class verticapy.machine_learning.vertica.svm.LinearSVC(name: str = None, overwrite_model: bool = False, tol: float = 0.0001, C: float = 1.0, intercept_scaling: float = 1.0, intercept_mode: Literal['regularized', 'unregularized'] = 'regularized', class_weight: Literal['auto', 'none'] | list = [1, 1], max_iter: int = 100)¶

Creates a LinearSVC object using the Vertica Support Vector Machine (SVM) algorithm on the data. Given a set of training examples, where each is marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

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.

tol: float, optional

Tolerance for stopping criteria. This is used to control accuracy.

C: float, optional

Weight for misclassification cost. The algorithm minimizes the regularization cost and the misclassification cost.

intercept_scaling: float

A float value, serves as the value of a dummy feature whose coefficient Vertica uses to calculate the model intercept. Because the dummy feature is not in the training data, its values are set to a constant, by default set to 1.

intercept_mode: str, optional

Specify how to treat the intercept.

regularized:
Fits the intercept and applies a regularization.
unregularized:
Fits the intercept but does not include it in regularization.

class_weight: str | list, optional

Specifies how to determine weights for the two classes. It can be a list of 2 elements or one of the following methods:

auto:
Weights each class according to the number of samples.
none:
No weights are used.

max_iter: int, optional

The maximum number of iterations that the algorithm performs.

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.

Model Initialization¶

First we import the LinearSVC model:

from verticapy.machine_learning.vertica import LinearSVC

Then we can create the model:

model = LinearSVC(
    tol = 1e-4,
    C = 1.0,
    intercept_scaling = 1.0,
    intercept_mode = "regularized",
    class_weight = [1, 1],
    max_iter = 100,
)

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
----------------+-----------
   Intercept    |  1.51057  
 fixed_acidity  |  0.01071  
volatile_acidity| -0.51127  
  citric_acid   |  0.12824  
 residual_sugar | -0.01789  
   chlorides    | -4.95417  
    density     | -1.70361  


===========
call_string
===========
SELECT svm_classifier('"public"."_verticapy_tmp_linearsvc_v_demo_dc64dddc55a411ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_dc77380655a411ef880f0242ac120002_"', '"good"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"'
USING PARAMETERS class_weights='1,1', C=1, max_iterations=100, intercept_mode='regularized', intercept_scaling=1, epsilon=0.0001);

===============
Additional Info
===============
       Name       |Value
------------------+-----
accepted_row_count|5192 
rejected_row_count|  0  
 iteration_count  | 13  

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	0.674371533196334
prc_auc	0.3028371815163968
accuracy	0.8130268199233717
log_loss	0.231551072438412
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.18697318007662833
csi	0.0

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	0.674371533196334
prc_auc	0.3028371815163968
accuracy	0.19770114942528735
log_loss	0.231551072438412
precision	0.1890007745933385
recall	1.0
f1_score	0.3179153094462541
mcc	0.049938801796684494
informedness	0.013195098963242113
markedness	0.18900077459333842
csi	0.1890007745933385

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[3]: 0.8130268199233717

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	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	0
2	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
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	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white	0
5	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	0
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
7	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	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	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white	0
10	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	0
11	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	0
12	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	0
13	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	0
14	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	0
15	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
16	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	0
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	0
18	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
19	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
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	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red	0
22	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	0
23	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	0
24	5.2	0.405	0.15	1.45	0.038	10.0	44.0	0.99125	3.52	0.4	11.6	4	0	white	0
25	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
26	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
27	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
28	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
29	5.3	0.32	0.12	6.6	0.043	22.0	141.0	0.9937	3.36	0.6	10.4	6	0	white	0
30	5.3	0.32	0.23	9.65	0.026	26.0	119.0	0.99168	3.18	0.53	12.2	6	0	white	0
31	5.3	0.57	0.01	1.7	0.054	5.0	27.0	0.9934	3.57	0.84	12.5	7	1	red	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	0.23	0.36	1.5	0.03	74.0	121.0	0.98976	3.24	0.99	12.1	7	1	white	0
34	5.4	0.42	0.27	2.0	0.092	23.0	55.0	0.99471	3.78	0.64	12.3	7	1	red	0
35	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white	0
36	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
37	5.5	0.16	0.22	4.5	0.03	30.0	102.0	0.9938	3.24	0.36	9.4	6	0	white	0
38	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white	0
39	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0
40	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
41	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
42	5.5	0.3	0.25	1.9	0.029	33.0	118.0	0.98972	3.36	0.66	12.5	6	0	white	0
43	5.5	0.42	0.09	1.6	0.019	18.0	68.0	0.9906	3.33	0.51	11.4	7	1	white	0
44	5.5	0.49	0.03	1.8	0.044	28.0	87.0	0.9908	3.5	0.82	14.0	8	1	red	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	0
46	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
47	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
48	5.6	0.19	0.39	1.1	0.043	17.0	67.0	0.9918	3.23	0.53	10.3	6	0	white	0
49	5.6	0.2	0.22	1.3	0.049	25.0	155.0	0.99296	3.74	0.43	10.0	5	0	white	0
50	5.6	0.2	0.36	2.5	0.048	16.0	125.0	0.99282	3.49	0.49	10.0	6	0	white	0
51	5.6	0.21	0.24	4.4	0.027	37.0	150.0	0.991	3.3	0.31	11.5	7	1	white	0
52	5.6	0.21	0.4	1.3	0.041	81.0	147.0	0.9901	3.22	0.95	11.6	8	1	white	0
53	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	0
54	5.6	0.245	0.25	9.7	0.032	12.0	68.0	0.994	3.31	0.34	10.5	5	0	white	0
55	5.6	0.25	0.19	2.4	0.049	42.0	166.0	0.992	3.25	0.43	10.4	6	0	white	0
56	5.6	0.25	0.26	3.6	0.037	18.0	115.0	0.9904	3.42	0.5	12.6	6	0	white	0
57	5.6	0.27	0.37	0.9	0.025	11.0	49.0	0.98845	3.29	0.33	13.1	6	0	white	0
58	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white	0
59	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
60	5.6	0.295	0.2	2.2	0.049	18.0	134.0	0.99378	3.21	0.68	10.0	5	0	white	0
61	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	0
62	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
63	5.6	0.34	0.25	2.5	0.046	47.0	182.0	0.99093	3.21	0.4	11.3	5	0	white	0
64	5.6	0.41	0.22	7.1	0.05	44.0	154.0	0.9931	3.3	0.4	10.5	5	0	white	0
65	5.6	0.54	0.04	1.7	0.049	5.0	13.0	0.9942	3.72	0.58	11.4	5	0	red	0
66	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	0
67	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
68	5.6	0.915	0.0	2.1	0.041	17.0	78.0	0.99346	3.68	0.73	11.4	5	0	red	0
69	5.7	0.15	0.28	3.7	0.045	57.0	151.0	0.9913	3.22	0.27	11.2	6	0	white	0
70	5.7	0.16	0.26	6.3	0.043	28.0	113.0	0.9936	3.06	0.58	9.9	6	0	white	0
71	5.7	0.18	0.22	4.2	0.042	25.0	111.0	0.994	3.35	0.39	9.4	5	0	white	0
72	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	0
73	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
74	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
75	5.7	0.25	0.21	1.5	0.044	21.0	108.0	0.99142	3.3	0.59	11.0	6	0	white	0
76	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
77	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
78	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
79	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
80	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
81	5.7	0.28	0.35	1.2	0.052	39.0	141.0	0.99108	3.44	0.69	11.3	6	0	white	0
82	5.7	0.31	0.28	4.1	0.03	22.0	86.0	0.99062	3.31	0.38	11.7	7	1	white	0
83	5.7	0.335	0.34	1.0	0.04	13.0	174.0	0.992	3.27	0.66	10.0	5	0	white	0
84	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
85	5.7	0.41	0.21	1.9	0.048	30.0	112.0	0.99138	3.29	0.55	11.2	6	0	white	0
86	5.7	0.43	0.3	5.7	0.039	24.0	98.0	0.992	3.54	0.61	12.3	7	1	white	0
87	5.7	0.44	0.13	7.0	0.025	28.0	173.0	0.9913	3.33	0.48	12.5	6	0	white	0
88	5.7	0.46	0.46	1.4	0.04	31.0	169.0	0.9932	3.13	0.47	8.8	5	0	white	0
89	5.8	0.17	0.3	1.4	0.037	55.0	130.0	0.9909	3.29	0.38	11.3	6	0	white	0
90	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
91	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
92	5.8	0.19	0.24	1.3	0.044	38.0	128.0	0.99362	3.77	0.6	10.6	5	0	white	0
93	5.8	0.19	0.49	4.9	0.04	44.0	118.0	0.9935	3.34	0.38	9.5	7	1	white	0
94	5.8	0.2	0.16	1.4	0.042	44.0	99.0	0.98912	3.23	0.37	12.2	6	0	white	0
95	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
96	5.8	0.23	0.2	2.0	0.043	39.0	154.0	0.99226	3.21	0.39	10.2	6	0	white	0
97	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
98	5.8	0.25	0.28	11.1	0.056	45.0	175.0	0.99755	3.42	0.43	9.5	5	0	white	0
99	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	0
100	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

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	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	0	0.662030305164294	0.337969694835706
2	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	0.594871947473055	0.405128052526945
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	0.677990092276227	0.322009907723774
4	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	0	0.63850906971726	0.36149093028274
5	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	0	0.629434427678696	0.370565572321304
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	0.648720139610313	0.351279860389687
7	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	0	0.573024092268253	0.426975907731747
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	0.6401392085145	0.3598607914855
9	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	0	0.629648200385675	0.370351799614325
10	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	0	0.651774792184336	0.348225207815664
11	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	0	0.637962820217989	0.362037179782011