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	3.9	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.

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    |  1.51553  
 fixed_acidity  |  0.00351  
volatile_acidity| -0.51234  
  citric_acid   |  0.14191  
 residual_sugar | -0.01813  
   chlorides    | -4.59023  
    density     | -1.68097  


===========
call_string
===========
SELECT svm_classifier('"public"."_verticapy_tmp_linearsvc_v_mldb_e198dc34979b11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_e1d74fd2979b11efa8720242ac120002_"', '"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|5196 
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	0.6752826149969873
prc_auc	0.2963134390332993
accuracy	0.8016910069177555
log_loss	0.235063722071299
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.19830899308224448
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	0.6752826149969873
prc_auc	0.2963134390332993
accuracy	0.20599538816295157
log_loss	0.235063722071299
precision	0.19984508133230056
recall	1.0
f1_score	0.3331181407359587
mcc	0.04377282517388779
informedness	0.009587727708533
markedness	0.1998450813323005
csi	0.19984508133230056

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]: 0.8016910069177555

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	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	0
2	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	0
3	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
4	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
5	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
6	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
7	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
8	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0
9	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0
10	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white	0
11	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	0
12	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
13	5.6	0.18	0.31	1.5	0.038	16.0	84.0	0.9924	3.34	0.58	10.1	6	0	white	0
14	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	0
15	5.6	0.46	0.24	4.8	0.042	24.0	72.0	0.9908	3.29	0.37	12.6	6	0	white	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	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	0
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	0.31	0.29	7.3	0.05	33.0	143.0	0.99332	3.31	0.5	11.0666666666667	6	0	white	0
20	5.8	0.22	0.25	1.5	0.024	21.0	109.0	0.99234	3.37	0.58	10.4	6	0	white	0
21	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
22	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	0
23	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
24	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	0
25	5.9	0.24	0.28	1.3	0.032	36.0	95.0	0.98889	3.08	0.64	12.9	7	1	white	0
26	5.9	0.27	0.27	9.0	0.051	43.0	136.0	0.9941	3.25	0.53	10.7	6	0	white	0
27	5.9	0.32	0.19	14.5	0.042	37.0	115.0	0.99684	3.16	0.43	10.3	5	0	white	0
28	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
29	5.9	0.34	0.31	2.0	0.03	38.0	142.0	0.98892	3.4	0.41	12.9	7	1	white	0
30	5.9	0.35	0.47	2.2	0.11	14.0	138.0	0.9932	3.09	0.5	9.1	5	0	white	0
31	5.9	0.435	0.16	6.4	0.031	21.0	134.0	0.99151	3.24	0.46	12.2	6	0	white	0
32	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
33	6.0	0.2	0.71	1.6	0.15	10.0	54.0	0.9927	3.12	0.47	9.8	5	0	white	0
34	6.0	0.24	0.27	1.9	0.048	40.0	170.0	0.9938	3.64	0.54	10.0	7	1	white	0
35	6.0	0.24	0.34	1.0	0.036	52.0	184.0	0.99097	3.44	0.44	11.45	6	0	white	0
36	6.0	0.24	0.41	1.3	0.036	42.0	118.0	0.99018	3.04	0.64	11.75	6	0	white	0
37	6.0	0.26	0.15	1.2	0.053	35.0	124.0	0.99347	3.08	0.46	8.8	5	0	white	0
38	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
39	6.0	0.32	0.3	1.9	0.033	41.0	142.0	0.98912	3.29	0.42	12.8	7	1	white	0
40	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
41	6.0	0.34	0.32	3.8	0.044	13.0	116.0	0.99108	3.39	0.44	11.8	7	1	white	0
42	6.0	0.34	0.66	15.9	0.046	26.0	164.0	0.9979	3.14	0.5	8.8	6	0	white	0
43	6.0	0.38	0.26	6.0	0.034	42.0	134.0	0.9912	3.38	0.38	12.3	7	1	white	0
44	6.0	0.58	0.2	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5	6	0	red	0
45	6.0	0.615	0.04	0.8	0.032	8.0	50.0	0.99036	3.14	0.4	11.0	4	0	white	0
46	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	0
47	6.1	0.28	0.25	12.9	0.054	34.0	189.0	0.9979	3.25	0.43	9.0	4	0	white	0
48	6.1	0.32	0.28	6.6	0.021	29.0	132.0	0.99188	3.15	0.36	11.45	7	1	white	0
49	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
50	6.1	0.38	0.14	3.9	0.06	27.0	113.0	0.99344	3.07	0.34	9.2	4	0	white	0
51	6.1	0.41	0.0	1.6	0.063	36.0	87.0	0.9914	3.27	0.67	10.8	6	0	white	0
52	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.3	7	1	white	0
53	6.1	0.68	0.52	1.4	0.037	32.0	123.0	0.99022	3.24	0.45	12.0	6	0	white	0
54	6.2	0.19	0.29	4.3	0.045	33.0	126.0	0.99658	3.18	0.42	9.3	6	0	white	0
55	6.2	0.2	0.28	1.1	0.039	24.0	78.0	0.9899	3.36	0.47	12.1	6	0	white	0
56	6.2	0.22	0.2	20.8	0.035	58.0	184.0	1.00022	3.11	0.53	9.0	6	0	white	0
57	6.2	0.23	0.35	0.7	0.051	24.0	111.0	0.9916	3.37	0.43	11.0	3	0	white	0
58	6.2	0.23	0.38	1.6	0.044	12.0	113.0	0.99176	3.3	0.73	11.4	5	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	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	0
61	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
62	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
63	6.2	0.33	0.14	4.8	0.052	27.0	128.0	0.99475	3.21	0.48	9.4	5	0	white	0
64	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
65	6.2	0.36	0.38	3.2	0.031	20.0	89.0	0.98956	3.06	0.33	12.0	7	1	white	0
66	6.2	0.44	0.39	2.5	0.077	6.0	14.0	0.99555	3.51	0.69	11.0	6	0	red	0
67	6.2	0.46	0.17	1.6	0.073	7.0	11.0	0.99425	3.61	0.54	11.4	5	0	red	0
68	6.2	0.56	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.6	11.3	5	0	red	0
69	6.2	0.63	0.31	1.7	0.088	15.0	64.0	0.9969	3.46	0.79	9.3	5	0	red	0
70	6.3	0.15	0.34	11.4	0.05	25.0	96.0	0.99754	3.21	0.49	10.0	6	0	white	0
71	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
72	6.3	0.18	0.22	1.5	0.043	45.0	155.0	0.99238	3.19	0.48	10.2	5	0	white	0
73	6.3	0.21	0.28	1.5	0.051	46.0	142.0	0.9928	3.23	0.42	10.1	6	0	white	0
74	6.3	0.23	0.22	17.45	0.054	42.0	151.0	0.99853	3.12	0.6	9.3	6	0	white	0
75	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
76	6.3	0.23	0.31	1.5	0.022	11.0	82.0	0.9892	3.3	0.4	12.9	7	1	white	0
77	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
78	6.3	0.26	0.29	2.2	0.043	35.0	175.0	0.9918	3.38	0.43	11.6	6	0	white	0
79	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	0
80	6.3	0.27	0.23	2.9	0.047	13.0	100.0	0.9936	3.28	0.43	9.8	5	0	white	0
81	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
82	6.3	0.29	0.28	4.7	0.059	28.0	81.0	0.99036	3.24	0.56	12.7	8	1	white	0
83	6.3	0.29	0.4	6.5	0.039	43.0	167.0	0.9953	3.15	0.44	9.3	6	0	white	0
84	6.3	0.3	0.2	3.7	0.039	34.0	132.0	0.99158	3.0	0.38	10.7	5	0	white	0
85	6.3	0.3	0.28	5.0	0.042	36.0	168.0	0.99505	3.22	0.69	9.5	6	0	white	0
86	6.3	0.36	0.19	3.2	0.075	15.0	39.0	0.9956	3.56	0.52	12.7	6	0	red	0
87	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
88	6.3	0.41	0.18	3.5	0.027	23.0	109.0	0.99018	3.34	0.54	12.8	8	1	white	0
89	6.3	0.68	0.01	3.7	0.103	32.0	54.0	0.99586	3.51	0.66	11.3	6	0	red	0
90	6.4	0.13	0.47	1.6	0.092	40.0	158.0	0.9928	3.21	0.36	9.8	6	0	white	0
91	6.4	0.15	0.25	7.8	0.05	13.0	68.0	0.99394	3.16	0.4	9.9	6	0	white	0
92	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
93	6.4	0.19	0.35	10.2	0.043	40.0	106.0	0.99632	3.16	0.5	9.7	6	0	white	0
94	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
95	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
96	6.4	0.29	0.57	1.0	0.06	15.0	120.0	0.9924	3.06	0.41	9.5	5	0	white	0
97	6.4	0.55	0.26	9.6	0.027	20.0	104.0	0.9924	3.22	0.73	13.1	6	0	white	0
98	6.5	0.08	0.33	1.9	0.028	23.0	93.0	0.991	3.34	0.7	12.0	7	1	white	0
99	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
100	6.5	0.19	0.27	4.9	0.037	13.0	101.0	0.9916	3.17	0.41	11.8	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	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white	0	0.616873090097416	0.383126909902585
2	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white	0	0.574286615140455	0.425713384859545
3	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white	0	0.61171075838902	0.38828924161098
4	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white	0	0.618369870123471	0.381630129876529
5	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white	0	0.636840547151148	0.363159452848852
6	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	0.575896690499756	0.424103309500244
7	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red	0	0.649881073320214	0.350118926679786
8	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white	0	0.60648552133803	0.39351447866197
9	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	0	0.719537161650835	0.280462838349165
10	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white	0	0.584022311304903	0.415977688695097
11	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	0	0.603730382380141	0.396269617619859
12	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	0.625791820260044	0.374208179739956
13	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	0.600134751396821	0.399865248603179
14	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white	0	0.610557875639967	0.389442124360033
15	5.8	0.18	0.37	1.1	0.036	31.0	96.0	0.98942	3.16	0.48	12.0	6	0	white	0	0.587156049126796	0.412843950873204
16	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	0.604249149947645	0.395750850052355
17	5.8	0.22	0.29	0.9	0.034	34.0	89.0	0.98936	3.14	0.36	11.1	7	1	white	0	0.591739088770114	0.408260911229886
18	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	0.595415835924887	0.404584164075113
19	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0	0.59901286925264	0.40098713074736
20	5.8	0.29	0.33	3.7	0.029	30.0	88.0	0.98994	3.25	0.42	12.3	6	0	white	0	0.605909086748409	0.394090913251591
21	5.9	0.14	0.25	4.5	0.027	34.0	140.0	0.9934	3.49	0.51	10.8	6	0	white	0	0.592773439924118	0.407226560075882
22	5.9	0.19	0.21	1.7	0.045	57.0	135.0	0.99341	3.32	0.44	9.5	5	0	white	0	0.60792529254203	0.39207470745797
23	5.9	0.21	0.23	7.9	0.033	22.0	130.0	0.9944	3.38	0.59	10.9	6	0	white	0	0.623639657708572	0.376360342291428
24	5.9	0.21	0.28	4.6	0.053	40.0	199.0	0.9964	3.72	0.7	10.0	4	0	white	0	0.630241181270479	0.369758818729521
25	5.9	0.22	0.45	22.6	0.12	55.0	122.0	0.99636	3.1	0.35	12.8	5	0	white	0	0.759173827098068	0.240826172901932
26	5.9	0.23	0.24	1.6	0.037	32.0	115.0	0.99076	3.21	0.51	11.4	6	0	white	0	0.601530461429781	0.398469538570219
27	5.9	0.29	0.33	7.4	0.037	58.0	205.0	0.99495	3.26	0.41	9.6	5	0	white	0	0.632286855010852	0.367713144989148
28	5.9	0.33	0.32	8.1	0.038	9.0	34.0	0.9911	3.22	0.36	12.7	7	1	white	0	0.639862131647028	0.360137868352972
29	5.9	0.37	0.32	1.6	0.029	41.0	102.0	0.98916	3.41	0.55	12.7	7	1	white	0	0.606544453973006	0.393455546026994
30	6.0	0.16	0.3	6.7	0.043	43.0	153.0	0.9951	3.63	0.46	10.6	5	0	white	0	0.621152093601499	0.378847906398501
31	6.0	0.2	0.26	6.8	0.049	22.0	93.0	0.9928	3.15	0.42	11.0	6	0	white	0	0.633230289306122	0.366769710693878
32	6.0	0.2	0.26	6.8	0.049	22.0	93.0	0.9928	3.15	0.42	11.0	6	0	white	0	0.633230289306122	0.366769710693878
33	6.0	0.25	0.4	5.7	0.052	56.0	152.0	0.99398	3.16	0.88	10.5	6	0	white	0	0.633591465189331	0.366408534810669
34	6.0	0.26	0.34	1.3	0.046	6.0	29.0	0.9924	3.29	0.63	10.4	5	0	white	0	0.610948981388323	0.389051018611677
35	6.0	0.4	0.3	1.6	0.047	30.0	117.0	0.9931	3.17	0.48	10.1	6	0	white	0	0.631792625447201	0.368207374552799
36	6.0	0.555	0.26	4.5	0.053	17.0	126.0	0.9943	3.24	0.46	9.1	5	0	white	0	0.669772048666899	0.330227951333101
37	6.0	0.67	0.07	1.2	0.06	9.0	108.0	0.9931	3.11	0.35	8.7	4	0	white	0	0.682071172076612	0.317928827923388
38	6.1	0.15	0.4	1.2	0.03	19.0	84.0	0.98926	3.19	0.96	13.0	6	0	white	0	0.575797326205141	0.424202673794859
39	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white	0	0.607406200880742	0.392593799119258
40	6.1	0.22	0.49	1.5	0.051	18.0	87.0	0.9928	3.3	0.46	9.6	5	0	white	0	0.607406200880742	0.392593799119258
41	6.1	0.25	0.3	1.2	0.036	42.0	107.0	0.991	3.34	0.56	10.8	7	1	white	0	0.599032671253498	0.400967328746502
42	6.1	0.28	0.27	8.0	0.048	41.0	162.0	0.99498	3.21	0.51	9.9	5	0	white	0	0.647062931472841	0.352937068527159
43	6.1	0.3	0.3	2.1	0.031	50.0	163.0	0.9895	3.39	0.43	12.7	7	1	white	0	0.602981142731046	0.397018857268954
44	6.1	0.3	0.32	2.2	0.042	41.0	142.0	0.98952	3.31	0.44	12.7	7	1	white	0	0.614769023325826	0.385230976674174
45	6.1	0.45	0.27	0.8	0.039	13.0	82.0	0.9927	3.23	0.32	9.5	5	0	white	0	0.626571579701481	0.373428420298519
46	6.2	0.16	0.32	1.1	0.036	74.0	184.0	0.99096	3.22	0.41	11.0	6	0	white	0	0.586679562764848	0.413320437235152
47	6.2	0.16	0.47	1.4	0.029	23.0	81.0	0.99	3.26	0.42	12.2	6	0	white	0	0.57460482343175	0.42539517656825
48	6.2	0.24	0.22	7.9	0.053	45.0	149.0	0.99545	3.23	0.52	9.3	5	0	white	0	0.648928505371754	0.351071494628246
49	6.2	0.27	0.26	12.1	0.046	43.0	127.0	0.9951	3.16	0.37	10.8	6	0	white	0	0.660937070946668	0.339062929053332
50	6.2	0.28	0.27	10.3	0.03	26.0	108.0	0.99388	3.2	0.36	10.7	6	0	white	0	0.63715566152877	0.36284433847123
51	6.2	0.34	0.28	7.5	0.034	40.0	197.0	0.99485	3.14	0.6	9.7	5	0	white	0	0.63681699353057	0.36318300646943
52	6.2	0.44	0.18	7.7	0.096	28.0	210.0	0.99771	3.56	0.72	9.2	5	0	white	0	0.71504816351329	0.28495183648671
53	6.2	0.45	0.2	1.6	0.069	3.0	15.0	0.9958	3.41	0.56	9.2	5	0	red	0	0.664752461715696	0.335247538284304
54	6.2	0.65	0.06	1.6	0.05	6.0	18.0	0.99348	3.57	0.54	11.95	5	0	red	0	0.671674368078653	0.328325631921347
55	6.2	0.66	0.48	1.2	0.029	29.0	75.0	0.9892	3.33	0.39	12.8	8	1	white	0	0.634238825665009	0.365761174334991
56	6.3	0.17	0.32	4.2	0.04	37.0	117.0	0.99182	3.24	0.43	11.3	6	0	white	0	0.606125070742021	0.393874929257979
57	6.3	0.21	0.29	11.7	0.048	49.0	147.0	0.99482	3.22	0.38	10.8	5	0	white	0	0.653300795231099	0.346699204768901
58	6.3	0.22	0.43	4.55	0.038	31.0	130.0	0.9918	3.35	0.33	11.5	7	1	white	0	0.607828362118117	0.392171637881883
59	6.3	0.29	0.29	3.3	0.037	32.0	140.0	0.9895	3.17	0.36	12.8	7	1	white	0	0.613678119195704	0.386321880804296
60	6.3	0.3	0.91	8.2	0.034	50.0	199.0	0.99394	3.39	0.49	11.7	6	0	white	0	0.613605027536123	0.386394972463877
61	6.3	0.38	0.17	8.8	0.08	50.0	212.0	0.99803	3.47	0.66	9.4	4	0	white	0	0.697917368161588	0.302082631838412
62	6.3	0.39	0.22	2.8	0.048	53.0	173.0	0.99304	3.24	0.45	9.8	5	0	white	0	0.639073135345884	0.360926864654116
63	6.3	0.98	0.01	2.0	0.057	15.0	33.0	0.99488	3.6	0.46	11.2	6	0	red	0	0.717749365811885	0.282250634188115
64	6.4	0.18	0.74	11.9	0.046	54.0	168.0	0.9978	3.58	0.68	10.1	5	0	white	0	0.634936716102441	0.365063283897559
65	6.4	0.22	0.34	1.4	0.023	56.0	115.0	0.98958	3.18	0.7	11.7	6	0	white	0	0.579544640663918	0.420455359336082
66	6.4	0.22	0.34	1.4	0.023	56.0	115.0	0.98958	3.18	0.7	11.7	6	0	white	0	0.579544640663918	0.420455359336082
67	6.4	0.25	0.3	5.5	0.038	15.0	129.0	0.9948	3.14	0.49	9.6	6	0	white	0	0.621032296376231	0.378967703623769
68	6.4	0.29	0.32	2.4	0.014	34.0	89.0	0.99008	3.24	0.66	12.5	7	1	white	0	0.583527207834189	0.416472792165811
69	6.4	0.3	0.27	5.0	0.058	27.0	151.0	0.99198	3.22	0.49	12.2	6	0	white	0	0.646068803545951	0.353931196454049
70	6.4	0.31	0.4	6.4	0.039	39.0	191.0	0.99513	3.14	0.52	9.8	5	0	white	0	0.629937304843422	0.370062695156578
71	6.4	0.33	0.3	7.2	0.041	42.0	168.0	0.99331	3.22	0.49	11.1	6	0	white	0	0.640380242471642	0.359619757528358
72	6.4	0.34	0.1	1.1	0.048	19.0	84.0	0.9927	3.21	0.38	9.8	5	0	white	0	0.629717324463351	0.370282675536649
73	6.4	0.35	0.21	2.1	0.051	46.0	171.0	0.9932	3.16	0.5	9.5	5	0	white	0	0.634892413033949	0.365107586966051
74	6.4	0.595	0.14	5.2	0.058	15.0	97.0	0.9951	3.38	0.36	9.0	4	0	white	0	0.685737220847759	0.314262779152241
75	6.5	0.13	0.27	2.6	0.035	32.0	76.0	0.9914	3.21	0.76	11.3333333333333	6	0	white	0	0.590076225783898	0.409923774216102
76	6.5	0.16	0.34	1.4	0.029	29.0	133.0	0.99108	3.33	0.64	11.5	7	1	white	0	0.579294006190545	0.420705993809455
77	6.5	0.18	0.41	14.2	0.039	47.0	129.0	0.99678	3.28	0.72	10.3	7	1	white	0	0.647438505869046	0.352561494130954
78	6.5	0.19	0.26	5.2	0.04	31.0	140.0	0.995	3.26	0.68	9.5	6	0	white	0	0.61599740943431	0.38400259056569
79	6.5	0.23	0.25	17.3	0.046	15.0	110.0	0.99828	3.15	0.42	9.2	6	0	white	0	0.678523112979792	0.321476887020208
80	6.5	0.23	0.33	13.8	0.042	25.0	139.0	0.99695	3.35	0.56	10.4	6	0	white	0	0.657364486009886	0.342635513990114
81	6.5	0.25	0.27	17.4	0.064	29.0	140.0	0.99776	3.2	0.49	10.1	6	0	white	0	0.698036455980868	0.301963544019132
82	6.5	0.28	0.29	2.7	0.038	26.0	107.0	0.9912	3.32	0.41	11.6	7	1	white	0	0.611480998754433	0.388519001245567
83	6.5	0.29	0.3	9.15	0.051	25.0	166.0	0.99339	3.24	0.56	11.35	6	0	white	0	0.654203986801655	0.345796013198345
84	6.5	0.29	0.31	1.7	0.035	24.0	79.0	0.99053	3.27	0.69	11.4	7	1	white	0	0.604151929425054	0.395848070574946
85	6.5	0.3	0.39	7.8	0.038	61.0	219.0	0.9959	3.19	0.5	9.4	5	0	white	0	0.63413228602264	0.36586771397736
86	6.5	0.32	0.3	2.3	0.051	20.0	127.0	0.98964	3.13	0.52	12.8	6	0	white	0	0.627711872020231	0.372288127979769
87	6.5	0.41	0.22	4.8	0.052	49.0	142.0	0.9946	3.14	0.62	9.2	5	0	white	0	0.654333330540308	0.345666669459692
88	6.5	0.51	0.25	1.7	0.048	39.0	177.0	0.99212	3.28	0.57	10.6	5	0	white	0	0.647112594972658	0.352887405027342
89	6.6	0.18	0.28	1.7	0.041	53.0	161.0	0.99207	3.13	0.45	10.2	6	0	white	0	0.59880157313858	0.40119842686142
90	6.6	0.21	0.36	0.8	0.034	48.0	113.0	0.99165	3.24	0.68	10.5	6	0	white	0	0.587910108390595	0.412089891609405
91	6.6	0.22	0.23	17.3	0.047	37.0	118.0	0.99906	3.08	0.46	8.8	6	0	white	0	0.679234998273593	0.320765001726407
92	6.6	0.22	0.23	17.3	0.047	37.0	118.0	0.99906	3.08	0.46	8.8	6	0	white	0	0.679234998273593	0.320765001726407
93	6.6	0.23	0.3	4.6	0.06	29.0	154.0	0.99142	3.23	0.49	12.2	8	1	white	0	0.636907282281135	0.363092717718865
94	6.6	0.23	0.3	14.9	0.051	33.0	118.0	0.99835	3.04	0.54	9.0	6	0	white	0	0.672406995417529	0.327593004582471
95	6.6	0.24	0.3	13.0	0.052	18.0	143.0	0.99825	3.37	0.49	9.4	6	0	white	0	0.666896402671748	0.333103597328252
96	6.6	0.25	0.31	12.4	0.059	52.0	181.0	0.9984	3.51	0.47	9.8	6	0	white	0	0.672472309122118	0.327527690877882
97	6.6	0.25	0.36	8.1	0.045	54.0	180.0	0.9958	3.08	0.42	9.2	5	0	white	0	0.637765575372973	0.362234424627027
98	6.6	0.31	0.28	1.4	0.035	28.0	107.0	0.98836	3.0	0.4	13.2	6	0	white	0	0.605362689039201	0.394637310960799
99	6.6	0.41	0.16	1.4	0.037	28.0	160.0	0.99167	2.95	0.45	10.6	6	0	white	0	0.625010132871653	0.374989867128347
100	6.6	0.42	0.13	12.8	0.044	26.0	158.0	0.99772	3.24	0.47	9.0	5	0	white	0	0.683366924384948	0.316633075615052

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[4]: 
array([[1043,    0],
       [ 258,    0]])

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()

Important

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[5]: 
{'tol': 0.0001,
 'C': 1.0,
 'intercept_scaling': 1.0,
 'intercept_mode': 'regularized',
 'class_weight': [1, 1],
 'max_iter': 100}

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[7]: '((1 / (1 + EXP(- (1.51553164629599 + 0.0035068366370968 * "fixed_acidity" + -0.512343741994686 * "volatile_acidity" + 0.141911729594543 * "citric_acid" + -0.0181348451555414 * "residual_sugar" + -4.59022886762405 * "chlorides" + -1.68096576167843 * "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[9]: 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, 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) → None¶

Methods

`__init__`([name, overwrite_model, tol, C, ...])
`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`