vastorbit.machine_learning.vast.linear_model.LogisticRegression

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

Creates a LogisticRegression 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.

Balancing the Dataset

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

To balance the dataset, use the following syntax.

from vastorbit.machine_learning.vast.preprocessing import balance

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

Note

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

Hint

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

Model Initialization

First we import the LogisticRegression model:

from vastorbit.machine_learning.vast import LogisticRegression

Then we can create the model:

model = LogisticRegression(
    tol = 1e-6,
    max_iter = 100,
    solver = 'newton-cg',
    fit_intercept = True,
)

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	0.6670646766169173
prc_auc	0.8024514098720474
accuracy	0.7905511811023622
log_loss	0.4588865938418838
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.20944881889763778
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	0.6491468392234953
prc_auc	0.8073252152046401
accuracy	0.5921259842519685
log_loss	0.4969701130957392
precision	0.24150268336314848
recall	0.5895196506550219
f1_score	0.34263959390862947
mcc	0.14112241786423338
informedness	0.18221897822466637
markedness	0.10929452583853516
csi	0.20673813169984687

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	6.3	0.67	0.48	12.6	0.052	57.0	222.0	0.9979	3.17	0.52	9.3	6	0	white	0.17	0
2	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	0.15	0
3	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	0.05	0
4	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	0.02	0
5	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	0.09	0
6	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	0.14	0
7	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	0.1	0
8	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	0.17	0
9	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	0.13	0
10	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white	0.0	0
11	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	0.12	0
12	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	0.0	0
13	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	0.1	0
14	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	0.07	0
15	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	0.17	0
16	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	0.11	0
17	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	0.05	0
18	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	0.06	0
19	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	0.07	0
20	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	0.0	0
21	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	0.15	0
22	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	0.17	0
23	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	0.02	0
24	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	0.01	0
25	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	0.1	0
26	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	0.0	0
27	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	0.06	0
28	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	0.09	0
29	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	0.13	0
30	8.1	0.2	0.49	8.1	0.051	51.0	205.0	0.9954	3.1	0.52	11.0	6	0	white	0.16	0
31	7.3	0.155	0.49	1.3	0.039	34.0	136.0	0.9926	3.14	0.77	10.5	6	0	white	0.13	0
32	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	0.12	0
33	7.1	0.18	0.74	15.6	0.044	44.0	176.0	0.9996	3.38	0.67	9.0	6	0	white	0.02	0
34	7.3	0.14	0.49	1.1	0.038	28.0	99.0	0.9928	3.2	0.72	10.6	6	0	white	0.15	0
35	6.7	0.29	0.49	4.7	0.034	35.0	156.0	0.9945	3.13	0.45	9.9	6	0	white	0.09	0
36	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
37	7.9	0.18	0.49	5.2	0.051	36.0	157.0	0.9953	3.18	0.48	10.6	6	0	white	0.05	0
38	8.5	0.17	0.49	8.8	0.048	23.0	108.0	0.9947	2.88	0.34	10.5	4	0	white	0.15	0
39	9.0	0.3	0.49	7.2	0.039	32.0	84.0	0.9938	2.94	0.32	11.5	6	0	white	0.12	0
40	6.4	0.25	0.74	7.8	0.045	52.0	209.0	0.9956	3.21	0.42	9.2	6	0	white	0.07	0
41	9.3	0.31	0.49	1.3	0.042	34.0	147.0	0.9948	3.11	0.46	9.8	5	0	white	0.02	0
42	6.4	0.27	0.49	7.3	0.046	53.0	206.0	0.9956	3.24	0.43	9.2	6	0	white	0.14	0
43	8.2	0.26	0.49	5.2	0.04	19.0	100.0	0.9941	3.12	0.34	10.1	6	0	white	0.07	0
44	8.0	0.29	0.49	11.7	0.035	40.0	131.0	0.9958	3.14	0.34	10.8	5	0	white	0.11	0
45	7.5	0.19	0.49	1.6	0.047	42.0	140.0	0.9932	3.4	0.47	10.7	6	0	white	0.07	0
46	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	0.11	0
47	6.4	0.37	0.49	13.3	0.045	53.0	243.0	0.9982	3.14	0.48	8.5	6	0	white	0.15	0
48	9.0	0.17	0.49	1.0	0.039	46.0	131.0	0.993	3.09	0.51	10.5	7	1	white	0.15	0
49	6.4	0.26	0.49	6.4	0.037	37.0	161.0	0.9954	3.38	0.53	9.7	6	0	white	0.18	0
50	8.2	0.2	0.49	3.5	0.057	14.0	108.0	0.9928	3.19	0.35	11.5	6	0	white	0.07	0
51	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	0.16	0
52	8.2	0.2	0.49	3.5	0.057	14.0	108.0	0.9928	3.19	0.35	11.5	6	0	white	0.06	0
53	7.6	0.47	0.49	13.0	0.239	42.0	220.0	0.9988	2.96	0.51	9.2	5	0	white	0.1	0
54	6.4	0.22	0.49	7.5	0.054	42.0	151.0	0.9948	3.27	0.52	10.1	6	0	white	0.18	0
55	8.1	0.3	0.49	12.3	0.049	50.0	144.0	0.9971	3.09	0.57	10.2	7	1	white	0.02	0
56	7.3	0.19	0.49	15.55	0.058	50.0	134.0	0.9998	3.42	0.36	9.1	7	1	white	0.06	0
57	6.6	0.46	0.49	7.4	0.052	19.0	184.0	0.9956	3.11	0.38	9.0	5	0	white	0.12	0
58	5.8	0.28	0.34	4.0	0.031	40.0	99.0	0.9896	3.39	0.39	12.8	7	1	white	0.09	0
59	7.4	0.21	0.27	1.2	0.041	27.0	99.0	0.9927	3.19	0.33	9.8	6	0	white	0.18	0
60	7.0	0.21	0.28	8.6	0.045	37.0	221.0	0.9954	3.25	0.54	10.4	6	0	white	0.04	0
61	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	0.0	0
62	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	0.11	0
63	7.1	0.32	0.32	11.0	0.038	16.0	66.0	0.9937	3.24	0.4	11.5	3	0	white	0.0	0
64	6.1	0.2	0.25	1.2	0.038	34.0	128.0	0.9921	3.24	0.44	10.1	5	0	white	0.06	0
65	7.2	0.29	0.53	18.15	0.047	59.0	182.0	0.9992	3.09	0.52	9.6	5	0	white	0.1	0
66	7.0	0.17	0.31	4.8	0.034	34.0	132.0	0.9944	3.36	0.48	9.6	7	1	white	0.06	0
67	7.4	0.28	0.36	1.1	0.028	42.0	105.0	0.9893	2.99	0.39	12.4	7	1	white	0.16	0
68	7.1	0.55	0.13	1.7	0.073	21.0	165.0	0.994	2.97	0.58	9.2	6	0	white	0.08	0
69	6.2	0.37	0.3	6.6	0.346	79.0	200.0	0.9954	3.29	0.58	9.6	5	0	white	0.11	0
70	7.2	0.4	0.62	10.8	0.041	70.0	189.0	0.9976	3.08	0.49	8.6	4	0	white	0.04	0
71	5.9	0.62	0.28	3.5	0.039	55.0	152.0	0.9907	3.44	0.44	12.0	6	0	white	0.06	0
72	7.4	0.36	0.33	1.4	0.025	27.0	55.0	0.9915	3.21	0.33	11.2	6	0	white	0.17	0
73	5.8	0.36	0.32	1.7	0.033	22.0	96.0	0.9898	3.03	0.38	11.2	6	0	white	0.13	0
74	5.6	0.35	0.37	1.0	0.038	6.0	72.0	0.9902	3.37	0.34	11.4	5	0	white	0.11	0
75	5.9	0.32	0.39	3.3	0.114	24.0	140.0	0.9934	3.09	0.45	9.2	6	0	white	0.02	0
76	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	0.09	0
77	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	0.05	0
78	6.7	0.31	0.31	9.9	0.04	10.0	175.0	0.9953	3.46	0.55	11.4	4	0	white	0.09	0
79	6.4	0.16	0.22	1.4	0.04	41.0	149.0	0.9933	3.49	0.58	10.0	6	0	white	0.06	0
80	7.0	0.3	0.51	13.6	0.05	40.0	168.0	0.9976	3.07	0.52	9.6	7	1	white	0.16	0
81	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	0.09	0
82	7.7	0.34	0.27	8.8	0.063	39.0	184.0	0.9969	3.09	0.63	9.2	6	0	white	0.11	0
83	6.5	0.28	0.27	5.2	0.04	44.0	179.0	0.9948	3.19	0.69	9.4	6	0	white	0.14	0
84	7.1	0.22	0.33	2.8	0.033	48.0	153.0	0.9899	3.15	0.38	12.7	7	1	white	0.1	0
85	7.4	0.26	0.3	7.9	0.049	38.0	157.0	0.9963	3.13	0.48	8.9	6	0	white	0.13	0
86	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	0.03	0
87	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	0.13	0
88	6.4	0.475	0.06	1.0	0.03	9.0	131.0	0.9904	2.97	0.29	10.8	5	0	white	0.07	0
89	6.3	0.26	0.49	1.5	0.052	34.0	134.0	0.9924	2.99	0.61	9.8	6	0	white	0.03	0
90	7.3	0.25	0.29	7.5	0.049	38.0	158.0	0.9965	3.43	0.38	9.6	5	0	white	0.12	0
91	6.1	0.28	0.25	17.75	0.044	48.0	161.0	0.9993	3.34	0.48	9.5	5	0	white	0.14	0
92	7.5	0.23	0.68	11.0	0.047	37.0	133.0	0.9978	2.99	0.38	8.8	5	0	white	0.05	0
93	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	0.17	0
94	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	0.05	0
95	6.0	0.36	0.06	1.4	0.066	27.0	128.0	0.9934	3.26	0.55	9.3	5	0	white	0.01	0
96	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	0.0	0
97	7.4	0.105	0.34	12.2	0.05	57.0	146.0	0.9973	3.16	0.37	9.0	6	0	white	0.05	0
98	7.1	0.26	0.34	14.4	0.067	35.0	189.0	0.9986	3.07	0.53	9.1	7	1	white	0.03	0
99	6.7	0.26	0.49	8.3	0.047	54.0	191.0	0.9954	3.23	0.4	10.3	6	0	white	0.16	0
100	6.2	0.27	0.32	8.8	0.047	65.0	224.0	0.9961	3.17	0.47	8.9	5	0	white	0.06	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	0.17	0	0.925067006160138	0.07493299383986202
2	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	0.15	0	0.7405609859764279	0.259439014023572
3	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	0.05	0	0.8550916211318254	0.1449083788681747
4	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	0.02	0	0.7708653981246472	0.2291346018753528
5	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	0.09	0	0.7195667162520554	0.2804332837479447
6	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	0.14	0	0.8495062129946531	0.15049378700534685
7	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	0.1	0	0.8009155065217811	0.19908449347821885
8	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	0.17	0	0.7193984964184351	0.2806015035815649
9	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	0.13	0	0.7086342357782163	0.29136576422178373
10	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white	0.0	0	0.6851852285077069	0.31481477149229303
11	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	0.12	0	0.686335148491818	0.313664851508182
12	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	0.0	0	0.7884131799945143	0.21158682000548568
13	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	0.1	0	0.7043192821169058	0.2956807178830942
14	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	0.07	0	0.8615829329086182	0.13841706709138174
15	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	0.17	0	0.9265237915618592	0.07347620843814089
16	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	0.11	0	0.7687847141185151	0.23121528588148488
17	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	0.05	0	0.7001800885733984	0.2998199114266016
18	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	0.06	0	0.6750476351507433	0.32495236484925677
19	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	0.07	0	0.7336881244004434	0.26631187559955655
20	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	0.0	0	0.7088617752434043	0.2911382247565957
21	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	0.15	0	0.9021646841652253	0.09783531583477473
22	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	0.17	0	0.7661894809958854	0.2338105190041146
23	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	0.02	0	0.6289678361098032	0.3710321638901967
24	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	0.01	0	0.7721411720820243	0.2278588279179757
25	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	0.1	0	0.6674074147696039	0.33259258523039614
26	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	0.0	0	0.8207460474289746	0.17925395257102536
27	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	0.06	0	0.8049949111179757	0.19500508888202422
28	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	0.09	0	0.7196637386404617	0.28033626135953826
29	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	0.13	0	0.8328177762557853	0.16718222374421463
30	8.1	0.2	0.49	8.1	0.051	51.0	205.0	0.9954	3.1	0.52	11.0	6	0	white	0.16	0	0.7794022330382359	0.22059776696176417
31	7.3	0.155	0.49	1.3	0.039	34.0	136.0	0.9926	3.14	0.77	10.5	6	0	white	0.13	0	0.6534395926948668	0.34656040730513316
32	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	0.12	0	0.7742469721272782	0.22575302787272183
33	7.1	0.18	0.74	15.6	0.044	44.0	176.0	0.9996	3.38	0.67	9.0	6	0	white	0.02	0	0.8125893663761938	0.1874106336238062
34	7.3	0.14	0.49	1.1	0.038	28.0	99.0	0.9928	3.2	0.72	10.6	6	0	white	0.15	0	0.6418933270491416	0.3581066729508584
35	6.7	0.29	0.49	4.7	0.034	35.0	156.0	0.9945	3.13	0.45	9.9	6	0	white	0.09	0	0.7491470455518884	0.2508529544481116
36	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	0.7645840846579658	0.23541591534203424
37	7.9	0.18	0.49	5.2	0.051	36.0	157.0	0.9953	3.18	0.48	10.6	6	0	white	0.05	0	0.7363584491681201	0.26364155083187996
38	8.5	0.17	0.49	8.8	0.048	23.0	108.0	0.9947	2.88	0.34	10.5	4	0	white	0.15	0	0.7756052659181399	0.22439473408186006
39	9.0	0.3	0.49	7.2	0.039	32.0	84.0	0.9938	2.94	0.32	11.5	6	0	white	0.12	0	0.8011917975247753	0.19880820247522474
40	6.4	0.25	0.74	7.8	0.045	52.0	209.0	0.9956	3.21	0.42	9.2	6	0	white	0.07	0	0.7502887436018149	0.24971125639818512
41	9.3	0.31	0.49	1.3	0.042	34.0	147.0	0.9948	3.11	0.46	9.8	5	0	white	0.02	0	0.7450524355610979	0.2549475644389021
42	6.4	0.27	0.49	7.3	0.046	53.0	206.0	0.9956	3.24	0.43	9.2	6	0	white	0.14	0	0.7804786844454522	0.2195213155545478
43	8.2	0.26	0.49	5.2	0.04	19.0	100.0	0.9941	3.12	0.34	10.1	6	0	white	0.07	0	0.7601074151215768	0.2398925848784233
44	8.0	0.29	0.49	11.7	0.035	40.0	131.0	0.9958	3.14	0.34	10.8	5	0	white	0.11	0	0.8314233509901761	0.168576649009824
45	7.5	0.19	0.49	1.6	0.047	42.0	140.0	0.9932	3.4	0.47	10.7	6	0	white	0.07	0	0.6860256351340668	0.3139743648659332
46	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	0.11	0	0.7002530130180978	0.29974698698190216
47	6.4	0.37	0.49	13.3	0.045	53.0	243.0	0.9982	3.14	0.48	8.5	6	0	white	0.15	0	0.8656320856772401	0.1343679143227598
48	9.0	0.17	0.49	1.0	0.039	46.0	131.0	0.993	3.09	0.51	10.5	7	1	white	0.15	0	0.6720682652888317	0.3279317347111683
49	6.4	0.26	0.49	6.4	0.037	37.0	161.0	0.9954	3.38	0.53	9.7	6	0	white	0.18	0	0.7578362716475279	0.24216372835247216
50	8.2	0.2	0.49	3.5	0.057	14.0	108.0	0.9928	3.19	0.35	11.5	6	0	white	0.07	0	0.7321723203176699	0.2678276796823301
51	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	0.16	0	0.7472356562200424	0.2527643437799576
52	8.2	0.2	0.49	3.5	0.057	14.0	108.0	0.9928	3.19	0.35	11.5	6	0	white	0.06	0	0.7321723203176699	0.2678276796823301
53	7.6	0.47	0.49	13.0	0.239	42.0	220.0	0.9988	2.96	0.51	9.2	5	0	white	0.1	0	0.9593848347180274	0.04061516528197258
54	6.4	0.22	0.49	7.5	0.054	42.0	151.0	0.9948	3.27	0.52	10.1	6	0	white	0.18	0	0.7710857028162902	0.2289142971837098
55	8.1	0.3	0.49	12.3	0.049	50.0	144.0	0.9971	3.09	0.57	10.2	7	1	white	0.02	0	0.8502580851457582	0.14974191485424185
56	7.3	0.19	0.49	15.55	0.058	50.0	134.0	0.9998	3.42	0.36	9.1	7	1	white	0.06	0	0.8481984455236817	0.1518015544763183
57	6.6	0.46	0.49	7.4	0.052	19.0	184.0	0.9956	3.11	0.38	9.0	5	0	white	0.12	0	0.849444691735979	0.150555308264021
58	5.6	0.32	0.33	7.4	0.037	25.0	95.0	0.99268	3.25	0.49	11.1	6	0	white	0.09	0	0.8015984472015294	0.19840155279847066
59	7.2	0.37	0.4	11.6	0.032	34.0	214.0	0.9963	3.1	0.51	9.8	6	0	white	0.18	0	0.8544692735563304	0.14553072644366966
60	7.1	0.18	0.39	14.5	0.051	48.0	156.0	0.99947	3.35	0.78	9.1	5	0	white	0.04	0	0.8383967732538583	0.16160322674614166
61	6.4	0.32	0.27	4.9	0.034	18.0	122.0	0.9916	3.36	0.71	12.5	6	0	white	0.0	0	0.784123380153061	0.21587661984693893
62	6.7	0.4	0.22	8.8	0.052	24.0	113.0	0.99576	3.22	0.45	9.4	5	0	white	0.11	0	0.8646845426701415	0.13531545732985856
63	6.6	0.15	0.32	6.0	0.033	59.0	128.0	0.99192	3.19	0.71	12.1	8	1	white	0.0	0	0.72332678377212	0.27667321622787994
64	6.6	0.21	0.29	5.35	0.029	43.0	106.0	0.99112	2.93	0.43	11.5	7	1	white	0.06	0	0.7401274116999939	0.2598725883000061
65	7.6	0.22	0.28	12.0	0.056	68.0	143.0	0.9983	2.99	0.3	9.2	6	0	white	0.1	0	0.8440154425849795	0.15598455741502049
66	6.5	0.3	0.27	4.0	0.038	37.0	97.0	0.99026	3.2	0.6	12.6	8	1	white	0.06	0	0.7709641712322165	0.2290358287677835
67	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	0.16	0	0.8133208349289004	0.18667916507109963
68	6.2	0.36	0.45	10.4	0.06	22.0	184.0	0.99711	3.31	0.56	9.8	6	0	white	0.08	0	0.8524606759169375	0.14753932408306242
69	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	0.11	0	0.8133208349289004	0.18667916507109963
70	6.1	0.33	0.3	3.0	0.036	30.0	124.0	0.98922	3.31	0.4	13.1	7	1	white	0.04	0	0.76312519004226	0.23687480995774007
71	6.7	0.29	0.45	14.3	0.054	30.0	181.0	0.99869	3.14	0.57	9.1	5	0	white	0.06	0	0.8625825496414713	0.13741745035852868
72	7.9	0.35	0.28	12.9	0.032	13.0	63.0	0.9932	2.99	0.43	13.0	6	0	white	0.17	0	0.8706090519210457	0.1293909480789543
73	6.0	0.23	0.15	9.7	0.048	101.0	207.0	0.99571	3.05	0.3	9.1	5	0	white	0.13	0	0.8223644411388416	0.17763555886115845
74	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	0.11	0	0.8003733065794396	0.19962669342056036
75	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	0.02	0	0.8003733065794396	0.19962669342056036
76	7.2	0.23	0.46	6.4	0.036	17.0	85.0	0.99279	3.1	0.78	11.7	6	0	white	0.09	0	0.753621589115617	0.2463784108843829
77	7.2	0.31	0.35	7.2	0.046	45.0	178.0	0.9955	3.14	0.53	9.7	5	0	white	0.05	0	0.8125062777206113	0.18749372227938865
78	7.8	0.28	0.25	3.4	0.024	27.0	99.0	0.98959	2.98	0.37	13.0	6	0	white	0.09	0	0.7541170440785165	0.2458829559214835
79	7.3	0.2	0.29	19.5	0.039	69.0	237.0	1.00037	3.1	0.48	9.2	6	0	white	0.06	0	0.8818601189648697	0.11813988103513026
80	7.2	0.14	0.32	1.1	0.022	48.0	116.0	0.99218	3.04	0.67	10.0	6	0	white	0.16	0	0.6444709858663717	0.3555290141336282
81	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	0.09	0	0.8671800806369114	0.13281991936308854
82	7.6	0.37	0.34	3.2	0.028	42.0	162.0	0.9903	3.01	0.33	12.4	6	0	white	0.11	0	0.7797996964553179	0.2202003035446821
83	8.0	0.25	0.27	9.7	0.036	15.0	85.0	0.99406	2.99	0.36	11.2	6	0	white	0.14	0	0.8203190373408136	0.17968096265918643
84	7.3	0.23	0.27	2.6	0.035	39.0	120.0	0.99138	3.04	0.59	11.3	7	1	white	0.1	0	0.729069064211219	0.270930935788781
85	5.8	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white	0.13	0	0.7653805616009228	0.23461943839907717
86	6.6	0.19	0.35	1.5	0.037	37.0	107.0	0.99006	3.18	0.68	12.0	7	1	white	0.03	0	0.6826320472382832	0.31736795276171686
87	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	0.13	0	0.7639162081710693	0.23608379182893072
88	6.8	0.19	0.32	7.6	0.049	37.0	107.0	0.99332	3.12	0.44	10.7	7	1	white	0.07	0	0.7765300674829971	0.22346993251700292
89	7.4	0.36	0.23	1.9	0.017	31.0	69.0	0.9892	2.93	0.36	12.5	6	0	white	0.03	0	0.7609937211804052	0.2390062788195948
90	6.8	0.14	0.18	1.4	0.047	30.0	90.0	0.99164	3.27	0.54	11.2	6	0	white	0.12	0	0.6935532864266438	0.3064467135733562
91	7.0	0.16	0.25	14.3	0.044	27.0	149.0	0.998	2.91	0.46	9.2	6	0	white	0.14	0	0.8363179094342419	0.16368209056575808
92	6.8	0.16	0.18	1.8	0.046	31.0	114.0	0.99226	3.27	0.55	10.8	6	0	white	0.05	0	0.70701048333542	0.29298951666458
93	7.0	0.16	0.25	14.3	0.044	27.0	149.0	0.998	2.91	0.46	9.2	6	0	white	0.17	0	0.8363179094342419	0.16368209056575808
94	7.0	0.29	0.33	0.9	0.041	20.0	117.0	0.99048	3.21	0.5	11.4	5	0	white	0.05	0	0.7304195635367168	0.26958043646328317
95	6.6	0.36	0.28	6.1	0.029	12.0	93.0	0.99054	3.19	0.27	12.8	7	1	white	0.01	0	0.8064299735582661	0.1935700264417339
96	6.5	0.26	0.39	1.4	0.02	12.0	66.0	0.99089	3.25	0.75	11.3	7	1	white	0.0	0	0.688471846277156	0.3115281537228441
97	6.4	0.29	0.24	3.2	0.037	31.0	95.0	0.98942	2.9	0.66	12.6	7	1	white	0.05	0	0.7593634483463253	0.24063655165367473
98	6.9	0.19	0.6	4.0	0.037	6.0	122.0	0.99255	2.92	0.59	10.4	4	0	white	0.03	0	0.6873228209027888	0.3126771790972111
99	6.7	0.26	0.49	8.3	0.047	54.0	191.0	0.9954	3.23	0.4	10.3	6	0	white	0.16	0	0.7903779813470129	0.20962201865298713
100	6.2	0.27	0.32	8.8	0.047	65.0	224.0	0.9961	3.17	0.47	8.9	5	0	white	0.06	0	0.8120683032808763	0.1879316967191237

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