vastorbit.machine_learning.vast.svm.LinearSVC

class vastorbit.machine_learning.vast.svm.LinearSVC(name: str = None, overwrite_model: bool = False, **kwargs)

Creates an LinearSVC object using scikit-learn for training and the scalability of VAST DataBase for the inferences.

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.

• **kwargs (scikit-learn model parameters.)

Variables:

• created (Many attributes are)

• phase. (during the fitting)

• 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.

• feature_importances_ (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.

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 vastorbit:

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit 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 vastorbit are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import vastorbit.datasets as vod

data = vod.load_winequality()

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white
2	7.4	0.4	0.29	5.4	0.044	31.0	122.0	0.994	3.3	0.5	11.1	8	1	white
3	7.1	0.26	0.31	2.2	0.044	29.0	128.0	0.9937	3.34	0.64	10.9	8	1	white
4	9.0	0.31	0.48	6.6	0.043	11.0	73.0	0.9938	2.9	0.38	11.6	5	0	white
5	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
6	8.2	0.22	0.36	6.8	0.034	12.0	90.0	0.9944	3.01	0.38	10.5	8	1	white
7	7.1	0.19	0.28	3.6	0.033	16.0	78.0	0.993	2.91	0.78	11.4	6	0	white
8	7.3	0.25	0.36	13.1	0.05	35.0	200.0	0.9986	3.04	0.46	8.9	7	1	white
9	7.9	0.2	0.34	1.2	0.04	29.0	118.0	0.9932	3.14	0.41	10.6	6	0	white
10	7.1	0.26	0.32	5.9	0.037	39.0	97.0	0.9934	3.31	0.4	11.6	6	0	white
11	7.0	0.2	0.34	5.7	0.035	32.0	83.0	0.9928	3.19	0.46	11.5	6	0	white
12	6.9	0.3	0.33	4.1	0.035	26.0	155.0	0.9925	3.25	0.79	12.3	8	1	white
13	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
14	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
15	5.9	0.415	0.02	0.8	0.038	22.0	63.0	0.9932	3.36	0.36	9.3	5	0	white
16	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white
17	8.6	0.55	0.35	15.55	0.057	35.5	366.5	1.0001	3.04	0.63	11.0	3	0	white
18	6.9	0.35	0.74	1.0	0.044	18.0	132.0	0.992	3.13	0.55	10.2	5	0	white
19	7.6	0.14	0.74	1.6	0.04	27.0	103.0	0.9916	3.07	0.4	10.8	7	1	white
20	9.2	0.28	0.49	11.8	0.042	29.0	137.0	0.998	3.1	0.34	10.1	4	0	white
21	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white
22	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
23	9.8	0.25	0.74	10.0	0.056	36.0	225.0	0.9977	3.06	0.43	10.0	4	0	white
24	8.1	0.29	0.49	7.1	0.042	22.0	124.0	0.9944	3.14	0.41	10.8	6	0	white
25	6.8	0.22	0.49	0.9	0.052	26.0	128.0	0.991	3.25	0.35	11.4	6	0	white
26	7.2	0.22	0.49	1.0	0.045	34.0	140.0	0.99	3.05	0.34	12.7	6	0	white
27	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
28	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
29	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
30	7.0	0.39	0.24	1.0	0.048	8.0	119.0	0.9923	3.0	0.31	10.1	4	0	white
31	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
32	6.5	0.36	0.49	2.9	0.03	16.0	94.0	0.9902	3.1	0.49	12.1	7	1	white
33	7.1	0.29	0.49	1.2	0.031	32.0	99.0	0.9893	3.07	0.33	12.2	6	0	white
34	7.4	0.25	0.49	1.1	0.042	35.0	156.0	0.9917	3.13	0.55	11.3	5	0	white
35	6.9	0.23	0.24	14.2	0.053	19.0	94.0	0.9982	3.17	0.5	9.6	5	0	white
36	8.5	0.56	0.74	17.85	0.051	51.0	243.0	1.0005	2.99	0.7	9.2	5	0	white
37	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white
38	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
39	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white
40	7.2	0.26	0.74	13.6	0.05	56.0	162.0	0.998	3.03	0.44	8.8	5	0	white
41	7.2	0.31	0.24	1.4	0.057	17.0	117.0	0.9928	3.16	0.35	10.5	5	0	white
42	8.0	0.25	0.49	1.2	0.061	27.0	117.0	0.9938	3.08	0.34	9.4	5	0	white
43	7.0	0.18	0.49	5.3	0.04	34.0	125.0	0.9914	3.24	0.4	12.2	6	0	white
44	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
45	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
46	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
47	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
48	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
49	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
50	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
51	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
52	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
53	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
54	8.3	0.21	0.49	19.8	0.054	50.0	231.0	1.0012	2.99	0.54	9.2	5	0	white
55	6.9	0.34	0.74	11.2	0.069	44.0	150.0	0.9968	3.0	0.81	9.2	5	0	white
56	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white
57	8.3	0.2	0.74	4.45	0.044	33.0	130.0	0.9924	3.25	0.42	12.2	6	0	white
58	7.1	0.22	0.74	2.7	0.044	42.0	144.0	0.991	3.31	0.41	12.2	6	0	white
59	7.9	0.11	0.49	4.5	0.048	27.0	133.0	0.9946	3.24	0.42	10.6	6	0	white
60	8.5	0.17	0.74	3.6	0.05	29.0	128.0	0.9928	3.28	0.4	12.4	6	0	white
61	6.4	0.145	0.49	5.4	0.048	54.0	164.0	0.9946	3.56	0.44	10.8	6	0	white
62	7.4	0.16	0.49	1.2	0.055	18.0	150.0	0.9917	3.23	0.47	11.2	6	0	white
63	8.3	0.19	0.49	1.2	0.051	11.0	137.0	0.9918	3.06	0.46	11.0	6	0	white
64	8.0	0.44	0.49	9.1	0.031	46.0	151.0	0.9926	3.16	0.27	12.7	8	1	white
65	7.0	0.2	0.74	0.8	0.044	19.0	163.0	0.9931	3.46	0.53	10.2	5	0	white
66	6.9	0.19	0.49	6.6	0.036	49.0	172.0	0.9932	3.2	0.27	11.5	6	0	white
67	7.1	0.25	0.49	3.0	0.03	30.0	96.0	0.9903	3.13	0.39	12.3	7	1	white
68	6.5	0.24	0.24	1.6	0.046	15.0	60.0	0.9928	3.19	0.39	9.8	5	0	white
69	7.2	0.4	0.49	1.1	0.048	11.0	138.0	0.9929	3.01	0.42	9.3	5	0	white
70	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white
71	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white
72	7.8	0.21	0.49	1.35	0.052	6.0	48.0	0.9911	3.15	0.28	11.4	5	0	white
73	7.0	0.2	0.49	5.9	0.038	39.0	128.0	0.9938	3.21	0.48	10.8	6	0	white
74	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
75	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
76	7.1	0.85	0.49	8.7	0.028	40.0	184.0	0.9962	3.22	0.36	10.7	5	0	white
77	7.6	0.51	0.24	1.2	0.04	10.0	104.0	0.992	3.05	0.29	10.8	6	0	white
78	7.9	0.22	0.24	4.6	0.044	39.0	159.0	0.9927	2.99	0.28	11.5	6	0	white
79	7.7	0.16	0.49	2.0	0.056	20.0	124.0	0.9948	3.32	0.49	10.7	6	0	white
80	7.2	0.08	0.49	1.3	0.05	18.0	148.0	0.9945	3.46	0.44	10.2	6	0	white
81	6.6	0.25	0.24	1.7	0.048	26.0	124.0	0.9942	3.37	0.6	10.1	6	0	white
82	6.7	0.16	0.49	2.4	0.046	57.0	187.0	0.9952	3.62	0.81	10.4	6	0	white
83	6.9	0.25	0.24	3.6	0.057	13.0	85.0	0.9942	2.99	0.48	9.5	4	0	white
84	7.5	0.32	0.24	4.6	0.053	8.0	134.0	0.9958	3.14	0.5	9.1	3	0	white
85	7.4	0.28	0.49	1.5	0.034	20.0	126.0	0.9918	2.98	0.39	10.6	6	0	white
86	6.2	0.15	0.49	0.9	0.033	17.0	51.0	0.9932	3.3	0.7	9.4	6	0	white
87	6.7	0.25	0.74	19.4	0.054	44.0	169.0	1.0004	3.51	0.45	9.8	6	0	white
88	6.5	0.26	0.74	13.3	0.044	68.0	224.0	0.9972	3.18	0.54	9.5	6	0	white
89	7.9	0.16	0.74	17.85	0.037	52.0	187.0	0.9998	2.99	0.41	9.3	5	0	white
90	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white
91	7.5	0.2	0.49	1.3	0.031	8.0	97.0	0.9918	3.06	0.62	11.1	5	0	white
92	8.0	0.3	0.49	9.4	0.046	47.0	188.0	0.9964	3.14	0.48	10.0	5	0	white
93	8.0	0.34	0.49	9.0	0.033	39.0	180.0	0.9936	3.13	0.38	12.3	8	1	white
94	7.7	0.35	0.49	8.65	0.033	42.0	186.0	0.9931	3.14	0.38	12.4	8	1	white
95	7.6	0.29	0.49	9.6	0.03	45.0	197.0	0.9938	3.13	0.38	12.3	7	1	white
96	6.7	0.62	0.24	1.1	0.039	6.0	62.0	0.9934	3.41	0.32	10.4	5	0	white
97	6.8	0.27	0.49	1.2	0.044	35.0	126.0	0.99	3.13	0.48	12.1	7	1	white
98	7.7	0.27	0.49	1.8	0.041	23.0	86.0	0.9914	3.16	0.42	12.5	6	0	white
99	6.7	0.51	0.24	2.1	0.043	14.0	155.0	0.9904	3.22	0.6	13.0	6	0	white
100	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white

Rows: 1-100 | Columns: 14

Note

vastorbit 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 vastorbit environment.

You can easily divide your dataset into training and testing subsets using the VastFrame.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 = vod.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, vastorbit 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 VastFrame.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 vastorbit.machine_learning.vast import LinearSVC

Then we can create the model:

model = LinearSVC(
    C = 1.0,
    intercept_scaling = 1.0,
    class_weight = [1, 1],
    max_iter = 1000,
)

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,
)

Important

To train a model, you can directly use the VastFrame 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 vastorbit, 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	0.6955280685061823
prc_auc	0.3106813995161213
accuracy	0.7978642257818459
log_loss	0.5347809560170647
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.2021357742181541
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	0.6747408175355465
prc_auc	0.795930129113751
accuracy	0.19374523264683446
log_loss	0.5521818379311714
precision	0.18754803996925443
recall	1.0
f1_score	0.31585760517799355
mcc	0.04192509498158747
informedness	0.009372071227741419
markedness	0.1875480399692544
csi	0.18754803996925443

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()

Prediction

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density"
    ],
    "prediction",
)

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 seedrand Decimal(26, 6)	123 prediction Integer
1	6.8	0.37	0.51	11.8	0.044	62.0	163.0	0.9976	3.19	0.44	8.8	5	0	white	0.05	0
2	7.2	0.25	0.39	18.95	0.038	42.0	155.0	0.9999	2.97	0.47	9.0	6	0	white	0.05	0
3	7.3	0.28	0.36	12.7	0.04	38.0	140.0	0.998	3.3	0.79	9.6	6	0	white	0.11	0
4	7.0	0.21	0.28	8.7	0.045	37.0	222.0	0.9954	3.25	0.54	10.4	6	0	white	0.08	0
5	7.8	0.21	0.27	1.2	0.051	20.0	89.0	0.9936	3.06	0.46	9.1	5	0	white	0.15	0
6	6.8	0.29	0.5	13.3	0.053	48.0	194.0	0.9974	3.09	0.45	9.4	5	0	white	0.18	0
7	6.1	0.28	0.32	2.5	0.042	23.0	218.5	0.9935	3.27	0.6	9.8	5	0	white	0.15	0
8	7.4	0.155	0.34	2.3	0.045	73.5	214.0	0.9934	3.18	0.61	9.9	7	1	white	0.1	0
9	6.2	0.35	0.04	1.2	0.06	23.0	108.0	0.9934	3.26	0.54	9.2	5	0	white	0.02	0
10	6.9	0.24	0.36	20.8	0.031	40.0	139.0	0.9975	3.2	0.33	11.0	6	0	white	0.01	0
11	5.9	0.46	0.14	2.7	0.042	27.0	160.0	0.9931	3.46	0.51	10.6	7	1	white	0.04	0
12	6.0	0.27	0.27	1.6	0.046	32.0	113.0	0.9924	3.41	0.51	10.5	7	1	white	0.1	0
13	6.1	0.43	0.35	9.1	0.059	83.0	249.0	0.9971	3.37	0.5	8.5	5	0	white	0.14	0
14	6.8	0.32	0.21	2.2	0.044	15.0	68.0	0.9932	3.17	0.39	9.4	6	0	white	0.05	0
15	6.6	0.24	0.29	2.0	0.023	19.0	86.0	0.99	3.25	0.45	12.5	6	0	white	0.17	0
16	6.3	0.22	0.33	1.7	0.041	67.0	164.0	0.9928	3.32	0.56	10.4	6	0	white	0.06	0
17	7.1	0.23	0.39	1.6	0.032	12.0	65.0	0.9898	3.25	0.4	12.7	7	1	white	0.12	0
18	6.8	0.19	0.58	14.2	0.038	51.0	164.0	0.9975	3.12	0.48	9.6	6	0	white	0.0	0
19	7.8	0.18	0.31	12.2	0.053	46.0	140.0	0.998	3.06	0.53	8.9	6	0	white	0.12	0
20	7.8	0.34	0.35	1.8	0.042	8.0	167.0	0.9908	3.11	0.41	12.1	6	0	white	0.11	0
21	7.8	0.28	0.32	9.0	0.036	34.0	115.0	0.9952	3.17	0.39	10.3	7	1	white	0.18	0
22	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	0.1	0
23	7.9	0.19	0.26	2.1	0.039	8.0	143.0	0.9942	3.05	0.74	9.8	5	0	white	0.13	0
24	6.9	0.25	0.4	1.3	0.038	22.0	101.0	0.9901	3.03	0.39	11.4	6	0	white	0.12	0
25	7.2	0.31	0.46	5.0	0.04	3.0	29.0	0.9906	3.04	0.53	12.5	4	0	white	0.16	0
26	7.4	0.25	0.36	13.2	0.067	53.0	178.0	0.9976	3.01	0.48	9.0	6	0	white	0.17	0
27	7.4	0.25	0.36	13.2	0.067	53.0	178.0	0.9976	3.01	0.48	9.0	6	0	white	0.06	0
28	7.2	0.46	0.65	10.4	0.05	76.0	192.0	0.9976	3.16	0.42	8.7	5	0	white	0.02	0
29	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white	0.19	0
30	6.5	0.18	0.31	1.7	0.044	30.0	127.0	0.9928	3.49	0.5	10.2	7	1	white	0.01	0
31	9.2	0.25	0.34	1.2	0.026	31.0	93.0	0.9916	2.93	0.37	11.3	7	1	white	0.08	0
32	7.8	0.28	0.34	1.6	0.028	32.0	118.0	0.9901	3.0	0.38	12.1	7	1	white	0.08	0
33	6.7	0.21	0.42	9.1	0.049	31.0	150.0	0.9953	3.12	0.74	9.9	7	1	white	0.08	0
34	8.2	0.22	0.3	1.8	0.047	47.0	185.0	0.9933	3.13	0.5	10.2	6	0	white	0.19	0
35	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	0.17	0
36	7.3	0.3	0.22	6.4	0.056	44.0	168.0	0.9947	3.13	0.35	10.1	6	0	white	0.03	0
37	7.1	0.31	0.5	14.5	0.059	6.0	148.0	0.9983	2.94	0.44	9.1	5	0	white	0.01	0
38	6.9	0.41	0.33	10.1	0.043	28.0	152.0	0.9968	3.2	0.52	9.4	5	0	white	0.0	0
39	6.4	0.45	0.07	1.1	0.03	10.0	131.0	0.9905	2.97	0.28	10.8	5	0	white	0.05	0
40	6.9	0.41	0.33	10.1	0.043	28.0	152.0	0.9968	3.2	0.52	9.4	5	0	white	0.14	0
41	7.2	0.24	0.4	1.4	0.045	31.0	106.0	0.9914	2.88	0.38	10.8	6	0	white	0.14	0
42	6.4	0.45	0.07	1.1	0.03	10.0	131.0	0.9905	2.97	0.28	10.8	5	0	white	0.02	0
43	7.5	0.21	0.68	10.9	0.045	38.0	133.0	0.9978	3.0	0.36	8.7	5	0	white	0.0	0
44	7.8	0.32	0.33	2.4	0.037	18.0	101.0	0.9912	3.21	0.65	11.7	7	1	white	0.18	0
45	7.8	0.26	0.27	1.9	0.051	52.0	195.0	0.9928	3.23	0.5	10.9	6	0	white	0.18	0
46	7.4	0.19	0.3	1.4	0.057	33.0	135.0	0.993	3.12	0.5	9.6	6	0	white	0.02	0
47	7.0	0.23	0.42	18.05	0.05	35.0	144.0	0.9999	3.22	0.42	8.8	5	0	white	0.11	0
48	6.2	0.17	0.28	4.7	0.037	39.0	133.0	0.9931	3.41	0.46	10.8	7	1	white	0.13	0
49	5.9	0.24	0.26	12.3	0.053	34.0	134.0	0.9972	3.34	0.45	9.5	6	0	white	0.09	0
50	6.6	0.27	0.25	3.1	0.052	41.0	188.0	0.9915	3.24	0.4	11.3	5	0	white	0.11	0
51	7.1	0.17	0.38	7.4	0.052	49.0	182.0	0.9958	3.35	0.52	9.6	6	0	white	0.03	0
52	6.2	0.36	0.26	13.2	0.051	54.0	201.0	0.9976	3.25	0.46	9.0	5	0	white	0.05	0
53	7.5	0.31	0.51	14.8	0.039	62.0	204.0	0.9982	3.06	0.6	9.5	5	0	white	0.15	0
54	6.2	0.31	0.21	6.3	0.041	50.0	218.0	0.9941	3.15	0.6	10.0	5	0	white	0.08	0
55	7.2	0.18	0.45	4.4	0.046	57.0	166.0	0.9943	3.13	0.62	11.2	6	0	white	0.07	0
56	8.2	0.27	0.43	1.6	0.035	31.0	128.0	0.9916	3.1	0.5	12.3	6	0	white	0.14	0
57	7.7	0.42	0.31	9.2	0.048	22.0	221.0	0.9969	3.06	0.61	9.2	6	0	white	0.11	0
58	6.9	0.31	0.34	1.6	0.032	23.0	128.0	0.9917	3.37	0.47	11.7	6	0	white	0.1	0
59	6.6	0.33	0.31	1.3	0.02	29.0	89.0	0.99035	3.26	0.44	12.4	8	1	white	0.0	0
60	7.8	0.3	0.4	1.8	0.028	23.0	122.0	0.9914	3.14	0.39	10.9	7	1	white	0.12	0
61	6.4	0.24	0.31	2.8	0.038	41.0	114.0	0.99155	3.37	0.66	11.7	7	1	white	0.14	0
62	7.0	0.21	0.34	8.0	0.057	19.0	101.0	0.9954	2.99	0.59	9.4	5	0	white	0.19	0
63	6.4	0.16	0.31	5.3	0.043	42.0	157.0	0.99455	3.35	0.47	10.5	5	0	white	0.01	0
64	8.8	0.28	0.45	6.0	0.022	14.0	49.0	0.9934	3.01	0.33	11.1	7	1	white	0.07	0
65	6.9	0.39	0.4	4.6	0.022	5.0	19.0	0.9915	3.31	0.37	12.6	3	0	white	0.16	0
66	7.4	0.38	0.27	7.5	0.041	24.0	160.0	0.99535	3.17	0.43	10.0	5	0	white	0.1	0
67	7.0	0.21	0.34	8.5	0.033	31.0	253.0	0.9953	3.22	0.56	10.5	6	0	white	0.0	0
68	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	0.02	0
69	7.1	0.13	0.4	1.2	0.047	54.0	134.0	0.9932	3.3	0.97	9.8	7	1	white	0.06	0
70	6.7	0.11	0.34	8.8	0.043	41.0	113.0	0.9962	3.42	0.4	9.3	7	1	white	0.01	0
71	8.1	0.17	0.44	14.1	0.053	43.0	145.0	1.0006	3.28	0.75	8.8	8	1	white	0.01	0
72	7.2	0.23	0.19	13.7	0.052	47.0	197.0	0.99865	3.12	0.53	9.0	5	0	white	0.0	0
73	7.7	0.34	0.28	11.0	0.04	31.0	117.0	0.99815	3.27	0.29	9.2	6	0	white	0.18	0
74	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	0.03	0
75	7.2	0.31	0.26	7.3	0.05	37.0	157.0	0.99625	3.09	0.43	9.0	5	0	white	0.19	0
76	7.8	0.42	0.23	8.8	0.054	42.0	215.0	0.9971	3.02	0.58	9.2	6	0	white	0.17	0
77	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	0.08	0
78	7.8	0.27	0.58	11.2	0.036	44.0	161.0	0.9977	3.06	0.41	8.9	6	0	white	0.05	0
79	6.2	0.29	0.26	13.1	0.046	55.0	204.0	0.99745	3.16	0.41	9.6	6	0	white	0.12	0
80	6.0	0.39	0.17	12.0	0.046	65.0	246.0	0.9976	3.15	0.38	9.0	6	0	white	0.16	0
81	6.2	0.3	0.26	13.4	0.046	57.0	206.0	0.99775	3.17	0.43	9.5	6	0	white	0.18	0
82	6.2	0.3	0.17	2.8	0.04	24.0	125.0	0.9939	3.01	0.46	9.0	5	0	white	0.15	0
83	6.2	0.3	0.17	2.8	0.04	24.0	125.0	0.9939	3.01	0.46	9.0	5	0	white	0.18	0
84	7.3	0.655	0.2	10.2	0.071	28.0	212.0	0.9971	2.96	0.58	9.2	6	0	white	0.0	0
85	6.9	0.25	0.34	1.3	0.035	27.0	82.0	0.99045	3.18	0.44	12.2	6	0	white	0.12	0
86	7.5	0.42	0.34	4.3	0.04	34.0	108.0	0.99155	3.14	0.45	12.8	8	1	white	0.07	0
87	7.3	0.25	0.36	2.1	0.034	30.0	177.0	0.99085	3.25	0.4	11.9	8	1	white	0.03	0
88	8.7	0.23	0.32	13.4	0.044	35.0	169.0	0.99975	3.12	0.47	8.8	7	1	white	0.04	0
89	6.6	0.22	0.37	15.4	0.035	62.0	153.0	0.99845	3.02	0.4	9.3	5	0	white	0.15	0
90	6.9	0.18	0.36	1.3	0.036	40.0	117.0	0.9934	3.27	0.95	9.5	7	1	white	0.18	0
91	6.2	0.28	0.33	1.7	0.029	24.0	111.0	0.99	3.24	0.5	12.1	6	0	white	0.08	0
92	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	0.16	0
93	6.1	0.27	0.33	2.2	0.021	26.0	117.0	0.9886	3.12	0.3	12.5	6	0	white	0.11	0
94	10.3	0.17	0.47	1.4	0.037	5.0	33.0	0.9939	2.89	0.28	9.6	3	0	white	0.05	0
95	8.8	0.36	0.44	1.9	0.04	9.0	121.0	0.9953	3.19	0.48	9.9	6	0	white	0.08	0
96	7.3	0.26	0.31	1.6	0.04	39.0	173.0	0.9918	3.19	0.51	11.4	6	0	white	0.12	0
97	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	0.02	0
98	6.8	0.18	0.28	8.7	0.047	52.0	242.0	0.9952	3.22	0.53	10.5	6	0	white	0.08	0
99	6.6	0.26	0.29	1.4	0.039	13.0	67.0	0.9915	3.05	0.49	10.9	6	0	white	0.04	0
100	6.6	0.2	0.38	7.9	0.052	30.0	145.0	0.9947	3.32	0.56	11.0	7	1	white	0.09	0

Rows: 1-100 | Columns: 16

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 VastFrame to the predict() function, but in this case, it’s essential that the column names of the VastFrame 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 Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 citric_acid Decimal(6, 3)	123 residual_sugar Decimal(7, 3)	123 chlorides Double	123 free_sulfur_dioxide Decimal(7, 2)	123 total_sulfur_dioxide Decimal(7, 2)	123 density Double	123 ph Decimal(6, 3)	123 sulphates Decimal(6, 3)	123 alcohol Double	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 seedrand Decimal(26, 6)	123 prediction Integer	123 prediction_0 Double	123 prediction_1 Double
1	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white	0.05	0	0.7058984705130045	0.29410152948699547
2	7.1	0.26	0.32	5.9	0.037	39.0	97.0	0.9934	3.31	0.4	11.6	6	0	white	0.05	0	0.6210228745525588	0.3789771254474413
3	7.0	0.2	0.34	5.7	0.035	32.0	83.0	0.9928	3.19	0.46	11.5	6	0	white	0.11	0	0.6098904641167214	0.39010953588327857
4	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white	0.08	0	0.610357558370861	0.38964244162913897
5	8.2	0.18	0.49	1.1	0.033	28.0	81.0	0.9923	3.0	0.68	10.4	7	1	white	0.15	0	0.5778433904991147	0.42215660950088524
6	7.0	0.39	0.24	1.0	0.048	8.0	119.0	0.9923	3.0	0.31	10.1	4	0	white	0.18	0	0.6307144791424906	0.3692855208575095
7	8.5	0.56	0.74	17.85	0.051	51.0	243.0	1.0005	2.99	0.7	9.2	5	0	white	0.15	0	0.7028741561698378	0.29712584383016216
8	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	0.1	0	0.631476271357604	0.368523728642396
9	6.1	0.25	0.49	7.6	0.052	67.0	226.0	0.9956	3.16	0.47	8.9	5	0	white	0.02	0	0.638423039918479	0.36157696008152096
10	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white	0.01	0	0.6595368337547927	0.3404631662452073
11	7.8	0.43	0.49	13.0	0.033	37.0	158.0	0.9955	3.14	0.35	11.3	6	0	white	0.04	0	0.6595368337547927	0.3404631662452073
12	7.6	0.52	0.49	14.0	0.034	37.0	156.0	0.9958	3.14	0.38	11.8	7	1	white	0.1	0	0.6749895707853908	0.3250104292146092
13	7.9	0.11	0.49	4.5	0.048	27.0	133.0	0.9946	3.24	0.42	10.6	6	0	white	0.14	0	0.6020843238723768	0.39791567612762324
14	7.1	0.25	0.49	3.0	0.03	30.0	96.0	0.9903	3.13	0.39	12.3	7	1	white	0.05	0	0.5919172078689977	0.4080827921310023
15	7.8	0.21	0.49	1.35	0.052	6.0	48.0	0.9911	3.15	0.28	11.4	5	0	white	0.17	0	0.6042293653085853	0.3957706346914147
16	7.7	0.16	0.49	2.0	0.056	20.0	124.0	0.9948	3.32	0.49	10.7	6	0	white	0.06	0	0.6065774693132939	0.3934225306867061
17	7.5	0.2	0.49	1.3	0.031	8.0	97.0	0.9918	3.06	0.62	11.1	5	0	white	0.12	0	0.5794665884660001	0.4205334115339999
18	8.3	0.2	0.49	1.7	0.04	34.0	169.0	0.9938	3.05	0.37	10.1	5	0	white	0.0	0	0.5912432081389749	0.40875679186102515
19	9.2	0.18	0.49	1.5	0.041	39.0	130.0	0.9945	3.04	0.49	9.8	7	1	white	0.12	0	0.5883549332592528	0.4116450667407472
20	8.8	0.23	0.74	3.2	0.042	15.0	126.0	0.9934	3.02	0.51	11.2	6	0	white	0.11	0	0.5931651647434732	0.40683483525652675
21	7.4	0.19	0.49	9.3	0.03	26.0	132.0	0.994	2.99	0.32	11.0	7	1	white	0.18	0	0.6126267275823272	0.3873732724176729
22	8.4	0.23	0.49	7.8	0.035	22.0	95.0	0.9935	3.04	0.34	12.0	6	0	white	0.1	0	0.6154706098271538	0.38452939017284615
23	8.3	0.2	0.49	1.7	0.038	38.0	167.0	0.9939	3.05	0.37	10.1	6	0	white	0.13	0	0.5890000271449743	0.41099997285502565
24	6.9	0.21	0.49	1.4	0.041	15.0	164.0	0.9927	3.25	0.63	11.0	5	0	white	0.12	0	0.5933669886645752	0.4066330113354248
25	14.2	0.27	0.49	1.1	0.037	33.0	156.0	0.992	3.15	0.54	11.1	6	0	white	0.16	0	0.587474433043416	0.41252556695658404
26	7.5	0.23	0.49	7.7	0.049	61.0	209.0	0.9941	3.14	0.3	11.1	7	1	white	0.17	0	0.6314988678703851	0.3685011321296149
27	7.3	0.26	0.49	5.0	0.028	32.0	107.0	0.9936	3.24	0.54	10.8	6	0	white	0.06	0	0.6003021442735257	0.39969785572647437
28	7.1	0.53	0.24	0.8	0.029	29.0	86.0	0.993	3.16	0.32	9.1	4	0	white	0.02	0	0.6257247786838867	0.37427522131611335
29	7.2	0.16	0.49	1.3	0.037	27.0	104.0	0.9924	3.23	0.57	10.6	6	0	white	0.19	0	0.5818491736485247	0.4181508263514752
30	7.1	0.36	0.24	1.8	0.025	32.0	102.0	0.9903	3.34	0.59	12.8	6	0	white	0.01	0	0.6043981760981878	0.3956018239018122
31	6.6	0.19	0.99	1.2	0.122	45.0	129.0	0.9936	3.09	0.31	8.7	6	0	white	0.08	0	0.6601890385283788	0.33981096147162115
32	7.3	0.21	0.49	1.8	0.038	44.0	152.0	0.9912	3.32	0.44	12.6	7	1	white	0.08	0	0.5908993470424897	0.40910065295751025
33	7.8	0.3	0.74	1.8	0.033	33.0	156.0	0.991	3.29	0.52	12.8	6	0	white	0.08	0	0.5857316055846882	0.4142683944153118
34	6.8	0.17	0.74	2.4	0.053	61.0	182.0	0.9953	3.63	0.76	10.5	6	0	white	0.19	0	0.5972824597244237	0.4027175402755762
35	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	0.17	0	0.6324180913344973	0.36758190866550267
36	8.4	0.24	0.49	7.4	0.039	46.0	108.0	0.9934	3.03	0.33	11.9	7	1	white	0.03	0	0.6193700240982719	0.3806299759017281
37	7.9	0.14	0.74	1.2	0.028	30.0	165.0	0.991	3.08	0.82	12.3	6	0	white	0.01	0	0.5573511919223473	0.4426488080776527
38	6.8	0.21	0.74	1.2	0.047	25.0	111.0	0.9916	3.13	0.41	10.7	6	0	white	0.0	0	0.5891197404894752	0.4108802595105248
39	8.6	0.16	0.49	7.3	0.043	9.0	63.0	0.9953	3.13	0.59	10.5	6	0	white	0.05	0	0.6141097790709811	0.3858902209290189
40	6.6	0.55	0.01	2.7	0.034	56.0	122.0	0.9906	3.15	0.3	11.9	5	0	white	0.14	0	0.6500196171707981	0.3499803828292019
41	8.2	0.34	0.49	8.0	0.046	55.0	223.0	0.996	3.08	0.52	10.7	6	0	white	0.14	0	0.6422516624905759	0.3577483375094241
42	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white	0.02	0	0.6318574558103611	0.36814254418963893
43	8.0	0.14	0.49	1.5	0.035	42.0	120.0	0.9928	3.26	0.4	10.6	7	1	white	0.0	0	0.5772853595634873	0.4227146404365127
44	6.9	0.34	0.49	7.3	0.045	61.0	206.0	0.9957	3.09	0.4	9.0	6	0	white	0.18	0	0.6394009509171754	0.36059904908282453
45	6.2	0.2	0.49	1.6	0.065	17.0	143.0	0.9937	3.22	0.52	9.2	6	0	white	0.18	0	0.6208427379550461	0.3791572620449539
46	6.2	0.22	0.49	6.0	0.029	31.0	128.0	0.9928	3.41	0.36	11.3	8	1	white	0.02	0	0.6017502657618697	0.39824973423813026
47	6.9	0.25	0.24	1.8	0.053	6.0	121.0	0.993	3.23	0.7	11.4	5	0	white	0.11	0	0.6232537549480187	0.3767462450519813
48	7.8	0.49	0.49	7.0	0.043	29.0	149.0	0.9952	3.21	0.33	10.0	5	0	white	0.13	0	0.6524503251530149	0.347549674846985
49	7.9	0.33	0.28	31.6	0.053	35.0	176.0	1.0103	3.15	0.38	8.8	6	0	white	0.09	0	0.7487417800116152	0.2512582199883848
50	6.6	0.31	0.49	7.7	0.05	52.0	220.0	0.9964	3.12	0.45	8.8	5	0	white	0.11	0	0.6434284990269306	0.35657150097306944
51	8.8	0.39	0.34	5.9	0.055	33.0	128.0	0.9927	2.95	0.51	11.8	6	0	white	0.03	0	0.6532423587711758	0.34675764122882424
52	7.2	0.25	0.28	14.4	0.055	55.0	205.0	0.9986	3.12	0.38	9.0	7	1	white	0.05	0	0.6768119192004892	0.3231880807995108
53	6.9	0.18	0.3	2.0	0.038	39.0	190.0	0.9914	3.32	0.37	12.2	6	0	white	0.15	0	0.5961144641946772	0.4038855358053228
54	6.4	0.24	0.22	1.5	0.038	38.0	157.0	0.9934	3.41	0.55	9.9	6	0	white	0.08	0	0.6054600277362545	0.3945399722637455
55	6.8	0.26	0.29	16.95	0.056	48.0	179.0	0.9998	3.45	0.4	9.6	5	0	white	0.07	0	0.6892015450645357	0.3107984549354643
56	7.9	0.18	0.37	3.0	0.061	25.0	178.0	0.995	3.22	0.51	10.0	6	0	white	0.14	0	0.6233683256256746	0.3766316743743255
57	7.0	0.21	0.31	6.0	0.046	29.0	108.0	0.9939	3.26	0.5	10.8	6	0	white	0.11	0	0.6260275549175909	0.3739724450824092
58	7.5	0.4	1.0	19.5	0.041	33.0	148.0	0.9977	3.24	0.38	12.0	6	0	white	0.1	0	0.6727467686543156	0.3272532313456844
59	6.5	0.18	0.34	1.6	0.04	43.0	148.0	0.9912	3.32	0.59	11.5	8	1	white	0.0	0	0.5953821009060227	0.4046178990939772
60	7.0	0.13	0.3	5.0	0.056	31.0	122.0	0.9945	3.47	0.42	10.5	6	0	white	0.12	0	0.6238273685025757	0.3761726314974243
61	7.0	0.3	0.32	6.4	0.034	28.0	97.0	0.9924	3.23	0.44	11.8	6	0	white	0.14	0	0.6244315570248313	0.3755684429751687
62	7.6	0.445	0.44	14.5	0.045	68.0	212.0	0.9986	3.48	0.36	10.0	6	0	white	0.19	0	0.6824252480437994	0.31757475195620055
63	6.8	0.3	0.24	6.6	0.123	35.0	116.0	0.9953	3.07	0.48	9.4	5	0	white	0.01	0	0.7205540587109842	0.27944594128901573
64	6.6	0.545	0.04	2.5	0.031	48.0	111.0	0.9906	3.14	0.32	11.9	5	0	white	0.07	0	0.6442678807165142	0.3557321192834858
65	6.6	0.37	0.47	6.5	0.061	23.0	150.0	0.9954	3.14	0.45	9.6	6	0	white	0.16	0	0.6576468086085813	0.34235319139141873
66	6.6	0.22	0.58	1.1	0.133	52.0	136.0	0.9932	3.1	0.3	9.1	5	0	white	0.1	0	0.6892064841781455	0.31079351582185455
67	7.4	0.23	0.25	1.4	0.049	43.0	141.0	0.9934	3.42	0.54	10.2	7	1	white	0.0	0	0.613960676170751	0.386039323829249
68	6.7	0.27	0.25	8.0	0.053	54.0	202.0	0.9961	3.22	0.43	9.3	5	0	white	0.02	0	0.6521139362443469	0.3478860637556531
69	6.6	0.62	0.2	8.7	0.046	81.0	224.0	0.99605	3.17	0.44	9.3	5	0	white	0.06	0	0.6887681680272848	0.31123183197271514
70	7.7	0.43	0.28	4.5	0.046	33.0	102.0	0.9918	3.16	0.56	12.2	7	1	white	0.01	0	0.6455239699882309	0.354476030011769
71	6.4	0.15	0.36	1.8	0.034	43.0	150.0	0.9922	3.42	0.69	11.0	8	1	white	0.01	0	0.5853420783226412	0.4146579216773588
72	6.7	0.24	0.29	6.8	0.038	54.0	127.0	0.9932	3.33	0.46	11.6	7	1	white	0.0	0	0.625054808125646	0.37494519187435404
73	7.2	0.26	0.3	2.1	0.033	50.0	158.0	0.9909	3.33	0.43	12.1	7	1	white	0.18	0	0.600232559039267	0.399767440960733
74	7.4	0.35	0.31	17.95	0.062	42.0	187.0	1.0002	3.27	0.64	9.1	5	0	white	0.03	0	0.7074474317824935	0.29255256821750647
75	6.7	0.37	0.41	6.3	0.061	22.0	149.0	0.9953	3.16	0.47	9.6	6	0	white	0.19	0	0.6589436376000469	0.3410563623999531
76	7.1	0.37	0.32	1.4	0.037	27.0	126.0	0.9918	3.19	0.62	12.0	5	0	white	0.17	0	0.6145623962636145	0.38543760373638547
77	6.4	0.26	0.21	7.1	0.04	35.0	162.0	0.9956	3.39	0.58	9.9	6	0	white	0.08	0	0.634882520042594	0.36511747995740595
78	7.6	0.3	0.22	10.2	0.049	57.0	191.0	0.9966	3.08	0.4	9.3	6	0	white	0.05	0	0.6606991219435596	0.33930087805644044
79	8.8	0.2	0.43	15.0	0.053	60.0	184.0	1.0008	3.28	0.79	8.8	6	0	white	0.12	0	0.6651843171502521	0.33481568284974794
80	7.5	0.33	0.39	12.4	0.065	29.0	119.0	0.9974	3.16	0.39	9.4	5	0	white	0.16	0	0.6834750993723369	0.3165249006276631
81	8.8	0.2	0.43	15.0	0.053	60.0	184.0	1.0008	3.28	0.79	8.8	6	0	white	0.18	0	0.6651843171502521	0.33481568284974794
82	6.8	0.3	0.35	2.8	0.038	10.0	164.0	0.9912	3.09	0.53	12.0	6	0	white	0.15	0	0.6122042545859976	0.3877957454140024
83	6.0	0.26	0.29	1.0	0.032	27.0	96.0	0.9896	3.38	0.44	12.3	6	0	white	0.18	0	0.5955379506783745	0.4044620493216256
84	7.4	0.29	0.31	1.7	0.035	23.0	110.0	0.9926	3.07	0.38	10.9	5	0	white	0.0	0	0.6042755790072197	0.39572442099278027
85	7.4	0.49	0.24	15.1	0.03	34.0	153.0	0.9953	3.13	0.51	12.0	7	1	white	0.12	0	0.6809684478169479	0.3190315521830521
86	8.0	0.55	0.42	12.6	0.211	37.0	213.0	0.9988	2.99	0.56	9.3	5	0	white	0.07	0	0.82708429605226	0.17291570394773997
87	6.5	0.34	0.36	11.0	0.052	53.0	247.0	0.9984	3.44	0.55	9.3	6	0	white	0.03	0	0.6679028607227369	0.3320971392772632
88	7.2	0.24	0.29	2.2	0.037	37.0	102.0	0.992	3.27	0.64	11.0	7	1	white	0.04	0	0.6034329987246496	0.39656700127535033
89	7.6	0.26	0.58	7.9	0.041	62.0	180.0	0.9966	3.07	0.38	9.0	5	0	white	0.15	0	0.6242330959455642	0.3757669040544358
90	6.4	0.18	0.31	1.6	0.049	36.0	127.0	0.9934	3.6	0.67	10.4	7	1	white	0.18	0	0.6074034514103513	0.39259654858964865
91	6.6	0.28	0.3	7.8	0.049	57.0	202.0	0.9958	3.24	0.39	9.5	5	0	white	0.08	0	0.6463044873270407	0.35369551267295923
92	7.1	0.25	0.3	2.4	0.042	25.0	122.0	0.994	3.43	0.61	10.5	6	0	white	0.16	0	0.6113566916726006	0.38864330832739935
93	7.6	0.36	0.44	8.3	0.255	28.0	142.0	0.9958	3.12	0.43	10.2	6	0	white	0.11	0	0.8309517484933275	0.1690482515066725
94	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	0.05	0	0.6877874800587385	0.31221251994126153
95	7.2	0.2	0.61	16.2	0.043	14.0	103.0	0.9987	3.06	0.36	9.2	6	0	white	0.08	0	0.6537642390430078	0.3462357609569922
96	7.2	0.24	0.4	17.85	0.049	50.0	185.0	1.0	3.34	0.42	9.6	5	0	white	0.12	0	0.6791970575425514	0.3208029424574485
97	7.9	0.26	0.33	10.3	0.039	73.0	212.0	0.9969	2.93	0.49	9.5	6	0	white	0.02	0	0.6415125795266023	0.35848742047339766
98	7.5	0.29	0.67	8.1	0.037	53.0	166.0	0.9966	2.9	0.41	8.9	6	0	white	0.08	0	0.6208526340390316	0.37914736596096843
99	7.2	0.31	0.41	8.6	0.053	15.0	89.0	0.9976	3.29	0.64	9.9	6	0	white	0.04	0	0.6531168493521193	0.3468831506478806
100	10.0	0.23	0.27	14.1	0.033	45.0	166.0	0.9988	2.72	0.43	9.7	6	0	white	0.09	0	0.6481119942128899	0.35188800578711005

Rows: 1-100 | Columns: 18

Note

Probabilities are added to the VastFrame, and vastorbit 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)

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()

And to manually change some of the parameters:

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

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()

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)

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, **kwargs) → None

Methods

__init__([name, overwrite_model])
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.
drop()	Drops the model from the VAST 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) to draw a specific graphic.
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.
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 VAST Binary format.
to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in VAST functions.
to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in VAST functions.

Attributes

classes_
object_type