vastorbit.machine_learning.vast.ensemble.RandomForestClassifier¶

class vastorbit.machine_learning.vast.ensemble.RandomForestClassifier(name: str = None, overwrite_model: bool = False, **kwargs)¶

Creates an RandomForestClassifier 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)
trees_ (list of BinaryTreeClassifier) – Tree models are instances of ` BinaryTreeClassifier, each possessing various attributes. For more detailed information, refer to the documentation for BinaryTreeClassifier.
feature_importances_ (numpy.array) – The importance of features. It is calculated using the MDI (Mean Decreased Impurity). To determine the final score, vastorbit sums the scores of each tree, normalizes them and applies an activation function to scale them. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.
feature_importances_trees_ (dict of numpy.array) – Each element of the array represents the feature importance of tree i. The importance of features is calculated using the MDI (Mean Decreased Impurity). It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.
n_estimators_ (int) – The number of model estimators.
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.

Important

Many tree-based models inherit from the RandomForest base class, and it’s recommended to use it directly for access to a wider range of options.

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 RandomForestClassifier model:

from vastorbit.machine_learning.vast import RandomForestClassifier

Then we can create the model:

model = RandomForestClassifier(
    n_estimators = 5,
    max_depth = 3,
)

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

In models such as RandomForest, feature importance is calculated using the MDI (Mean Decreased Impurity). To determine the final score, vastorbit sums the scores of each tree, normalizes them and applies an activation function to scale them.

Metrics¶

We can get the entire report using:

model.report()

	value
auc	0.0
prc_auc	0.0
accuracy	0.0007633587786259542
log_loss	0.024025609155781325
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	-0.999236641221374
markedness	0.0
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	0.0
prc_auc	0.0
accuracy	0.0
log_loss	0.02088795914299869
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	-1.0
markedness	-1.0
csi	0.0

Rows: 1-11 | Columns: 2

You can also use the 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 Decimal(11, 1)
1	7.5	0.705	0.1	13.0	0.044	44.0	214.0	0.99741	3.1	0.5	9.1	5	0	white	0.12	1.0
2	7.2	0.24	0.29	3.0	0.036	17.0	117.0	0.99411	3.36	0.68	10.1	6	0	white	0.17	1.0
3	7.3	0.22	0.26	1.5	0.04	32.0	172.0	0.99194	3.27	0.48	11.2	6	0	white	0.05	1.0
4	8.1	0.34	0.28	7.5	0.04	70.0	230.0	0.99558	3.14	0.55	9.8	6	0	white	0.11	1.0
5	8.0	0.26	0.28	4.8	0.05	34.0	150.0	0.99437	3.13	0.5	10.0	6	0	white	0.03	1.0
6	7.3	0.27	0.37	9.7	0.042	36.0	130.0	0.9979	3.48	0.75	9.9	6	0	white	0.06	1.0
7	6.8	0.46	0.26	6.3	0.147	49.0	159.0	0.99434	3.14	0.47	10.0	5	0	white	0.06	1.0
8	7.2	0.2	0.28	1.6	0.028	13.0	168.0	0.99203	3.17	1.06	11.5	6	0	white	0.1	1.0
9	7.6	0.285	0.32	14.6	0.063	32.0	201.0	0.998	3.0	0.45	9.2	5	0	white	0.11	1.0
10	7.6	0.285	0.32	14.6	0.063	32.0	201.0	0.998	3.0	0.45	9.2	5	0	white	0.07	1.0
11	8.2	0.26	0.33	2.6	0.053	11.0	71.0	0.99402	2.89	0.49	9.5	5	0	white	0.17	1.0
12	5.6	0.35	0.4	6.3	0.022	23.0	174.0	0.9922	3.54	0.5	11.6	7	1	white	0.19	1.0
13	6.1	0.27	0.31	1.5	0.035	17.0	83.0	0.99076	3.32	0.44	11.1	7	1	white	0.18	1.0
14	6.3	0.27	0.37	7.9	0.047	58.0	215.0	0.99542	3.19	0.48	9.5	6	0	white	0.18	1.0
15	6.0	0.2	0.24	5.3	0.075	49.0	201.0	0.99466	3.21	0.43	9.5	5	0	white	0.17	1.0
16	6.9	0.26	0.31	7.0	0.039	37.0	175.0	0.99376	3.32	0.49	11.4	6	0	white	0.09	1.0
17	8.6	0.34	0.36	1.4	0.045	11.0	119.0	0.99556	3.17	0.47	9.4	4	0	white	0.04	1.0
18	7.1	0.12	0.3	3.1	0.018	15.0	37.0	0.99004	3.02	0.52	11.9	7	1	white	0.16	1.0
19	6.3	0.24	0.55	8.1	0.04	67.0	216.0	0.99596	3.24	0.5	9.2	5	0	white	0.01	1.0
20	8.2	0.2	0.38	3.5	0.053	41.0	174.0	0.99306	3.22	0.41	11.6	5	0	white	0.04	1.0
21	7.5	0.41	0.23	14.8	0.054	28.0	174.0	0.99898	3.18	0.49	9.7	5	0	white	0.15	1.0
22	6.7	0.54	0.27	7.1	0.049	8.0	178.0	0.99502	3.16	0.38	9.4	4	0	white	0.08	1.0
23	5.9	0.28	0.14	8.6	0.032	30.0	142.0	0.99542	3.28	0.44	9.5	6	0	white	0.19	1.0
24	5.9	0.28	0.14	8.6	0.032	30.0	142.0	0.99542	3.28	0.44	9.5	6	0	white	0.03	1.0
25	6.9	0.32	0.3	1.8	0.036	28.0	117.0	0.99269	3.24	0.48	11.0	6	0	white	0.17	1.0
26	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	0.19	1.0
27	6.3	0.26	0.42	7.1	0.045	62.0	209.0	0.99544	3.2	0.53	9.5	6	0	white	0.11	1.0
28	7.6	0.23	0.64	12.9	0.033	54.0	170.0	0.998	3.0	0.53	8.8	5	0	white	0.07	1.0
29	6.6	0.24	0.24	8.6	0.034	25.0	135.0	0.99582	3.33	0.59	10.3	6	0	white	0.03	1.0
30	6.6	0.24	0.28	1.8	0.028	39.0	132.0	0.99182	3.34	0.46	11.4	5	0	white	0.05	1.0
31	7.0	0.14	0.28	1.3	0.026	10.0	56.0	0.99352	3.46	0.45	9.9	5	0	white	0.07	1.0
32	6.8	0.26	0.24	1.9	0.043	70.0	154.0	0.99273	3.18	0.52	10.5	5	0	white	0.03	1.0
33	6.5	0.27	0.4	10.0	0.039	74.0	227.0	0.99582	3.18	0.5	9.4	5	0	white	0.09	1.0
34	6.8	0.18	0.3	12.8	0.062	19.0	171.0	0.99808	3.0	0.52	9.0	7	1	white	0.15	1.0
35	6.8	0.18	0.3	12.8	0.062	19.0	171.0	0.99808	3.0	0.52	9.0	7	1	white	0.09	1.0
36	7.3	0.32	0.29	1.5	0.038	32.0	144.0	0.99296	3.2	0.55	10.8	5	0	white	0.14	1.0
37	7.8	0.28	0.31	2.1	0.046	28.0	208.0	0.99434	3.23	0.64	9.8	5	0	white	0.17	1.0
38	7.1	0.2	0.3	0.9	0.019	4.0	28.0	0.98931	3.2	0.36	12.0	6	0	white	0.12	1.0
39	6.15	0.21	0.37	3.2	0.021	20.0	80.0	0.99076	3.39	0.47	12.0	5	0	white	0.12	1.0
40	6.5	0.28	0.28	20.4	0.041	40.0	144.0	1.0002	3.14	0.38	8.7	5	0	white	0.1	1.0
41	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	0.03	1.0
42	6.7	0.28	0.28	2.4	0.012	36.0	100.0	0.99064	3.26	0.39	11.7	7	1	white	0.02	1.0
43	8.2	0.32	0.26	2.1	0.062	26.0	87.0	0.98974	3.1	0.47	12.8	6	0	white	0.16	1.0
44	8.7	0.31	0.73	14.35	0.044	27.0	191.0	1.00013	2.96	0.88	8.7	5	0	white	0.11	1.0
45	6.7	0.12	0.3	5.2	0.048	38.0	113.0	0.99352	3.33	0.44	10.1	7	1	white	0.11	1.0
46	5.8	0.15	0.28	0.8	0.037	43.0	127.0	0.99198	3.24	0.51	9.3	5	0	white	0.02	1.0
47	6.6	0.23	0.29	14.45	0.057	29.0	144.0	0.99756	3.33	0.54	10.2	6	0	white	0.08	1.0
48	6.4	0.28	0.27	11.0	0.042	45.0	148.0	0.99786	3.14	0.46	8.7	5	0	white	0.17	1.0
49	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	0.16	1.0
50	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	0.04	1.0
51	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	0.11	1.0
52	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	0.14	1.0
53	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	0.14	1.0
54	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	1.0
55	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	0.06	1.0
56	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	0.13	1.0
57	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	0.07	1.0
58	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	0.08	1.0
59	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	0.0	1.0
60	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	0.17	1.0
61	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	0.02	1.0
62	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	1.0
63	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	0.14	1.0
64	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	0.02	1.0
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	0.14	1.0
66	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	0.17	1.0
67	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	0.0	1.0
68	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	0.0	1.0
69	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	0.06	1.0
70	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	0.16	1.0
71	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	0.04	1.0
72	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	0.18	1.0
73	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	0.05	1.0
74	6.6	0.3	0.24	1.2	0.034	17.0	121.0	0.9933	3.13	0.36	9.2	5	0	white	0.06	1.0
75	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	0.12	1.0
76	6.6	0.3	0.24	1.2	0.034	17.0	121.0	0.9933	3.13	0.36	9.2	5	0	white	0.19	1.0
77	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	0.05	1.0
78	7.9	0.28	0.49	7.7	0.045	48.0	195.0	0.9954	3.04	0.55	11.0	6	0	white	0.13	1.0
79	7.2	0.27	0.74	12.5	0.037	47.0	156.0	0.9981	3.04	0.44	8.7	5	0	white	0.08	1.0
80	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	0.04	1.0
81	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	0.12	1.0
82	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	0.17	1.0
83	7.9	0.12	0.49	5.2	0.049	33.0	152.0	0.9952	3.18	0.47	10.6	6	0	white	0.19	1.0
84	6.9	0.24	0.49	1.3	0.032	35.0	148.0	0.9932	3.45	0.57	10.7	7	1	white	0.12	1.0
85	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	0.18	1.0
86	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	0.07	1.0
87	7.1	0.64	0.49	1.8	0.05	17.0	128.0	0.9946	3.31	0.58	10.6	4	0	white	0.04	1.0
88	9.8	0.31	0.49	15.4	0.046	13.0	119.0	1.0004	3.18	0.45	9.5	5	0	white	0.01	1.0
89	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	0.14	1.0
90	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	0.1	1.0
91	6.3	0.24	0.74	1.4	0.172	24.0	108.0	0.9932	3.27	0.39	9.9	6	0	white	0.17	1.0
92	5.6	0.41	0.24	1.9	0.034	10.0	53.0	0.98815	3.32	0.5	13.5	7	1	white	0.07	1.0
93	7.5	0.32	0.49	1.7	0.031	44.0	109.0	0.9906	3.07	0.46	12.5	6	0	white	0.14	1.0
94	8.9	0.32	0.49	1.6	0.05	17.0	131.0	0.9956	3.13	0.34	9.4	5	0	white	0.05	1.0
95	9.0	0.22	0.49	10.4	0.048	52.0	195.0	0.9987	3.31	0.44	10.2	6	0	white	0.17	1.0
96	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	0.1	1.0
97	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	0.14	1.0
98	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	0.0	1.0
99	7.4	0.19	0.49	6.7	0.037	15.0	110.0	0.9938	3.2	0.38	11.0	7	1	white	0.13	1.0
100	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	1.0

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 Decimal(11, 1)	123 prediction_00 Decimal(17, 12)	123 prediction_10 Decimal(17, 12)
1	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	0.12	1.0	1.0	1.0
2	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	0.17	1.0	1.0	1.0
3	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	0.05	1.0	1.0	1.0
4	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	0.11	1.0	1.0	1.0
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	0.03	1.0	1.0	1.0
6	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	0.06	1.0	1.0	1.0
7	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	0.06	1.0	1.0	1.0
8	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	0.1	1.0	1.0	1.0
9	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	0.11	1.0	1.0	1.0
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	0.07	1.0	1.0	1.0
11	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	0.17	1.0	1.0	1.0
12	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	0.19	1.0	1.0	1.0
13	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	0.18	1.0	1.0	1.0
14	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	0.18	1.0	1.0	1.0
15	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	0.17	1.0	1.0	1.0
16	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	0.09	1.0	1.0	1.0
17	6.8	0.36	0.24	4.6	0.039	24.0	124.0	0.9909	3.27	0.34	12.6	7	1	white	0.04	1.0	0.8	0.8
18	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	0.16	1.0	1.0	1.0
19	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	0.01	1.0	1.0	1.0
20	8.0	0.25	0.49	9.0	0.044	31.0	185.0	0.998	3.34	0.49	10.0	6	0	white	0.04	1.0	1.0	1.0
21	6.6	0.21	0.49	18.15	0.042	41.0	158.0	0.9997	3.28	0.39	8.7	6	0	white	0.15	1.0	1.0	1.0
22	8.1	0.3	0.49	8.1	0.037	26.0	174.0	0.9943	3.1	0.3	11.2	7	1	white	0.08	1.0	1.0	1.0
23	8.1	0.28	0.49	1.0	0.04	32.0	148.0	0.9936	3.13	0.41	10.0	6	0	white	0.19	1.0	1.0	1.0
24	6.0	0.17	0.49	1.0	0.034	26.0	106.0	0.992	3.21	0.42	9.8	6	0	white	0.03	1.0	1.0	1.0
25	8.9	0.13	0.49	1.0	0.028	6.0	24.0	0.9926	2.91	0.32	9.9	5	0	white	0.17	1.0	1.0	1.0
26	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	0.19	1.0	1.0	1.0
27	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	0.11	1.0	1.0	1.0
28	7.6	0.23	0.49	10.0	0.036	45.0	182.0	0.9967	3.08	0.58	9.6	6	0	white	0.07	1.0	1.0	1.0
29	6.7	0.3	0.74	5.0	0.038	35.0	157.0	0.9945	3.21	0.46	9.9	5	0	white	0.03	1.0	1.0	1.0
30	6.6	0.3	0.74	4.6	0.041	36.0	159.0	0.9946	3.21	0.45	9.9	5	0	white	0.05	1.0	1.0	1.0
31	6.9	0.22	0.49	7.0	0.063	50.0	168.0	0.9957	3.54	0.5	10.3	6	0	white	0.07	1.0	1.0	1.0
32	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	0.03	1.0	1.0	1.0
33	6.2	0.27	0.49	1.4	0.05	20.0	74.0	0.9931	3.32	0.44	9.8	6	0	white	0.09	1.0	1.0	1.0
34	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	0.15	1.0	1.0	1.0
35	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	0.09	1.0	1.0	1.0
36	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	0.14	1.0	1.0	1.0
37	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	0.17	1.0	1.0	1.0
38	8.1	0.2	0.49	11.8	0.048	46.0	212.0	0.9968	3.09	0.46	10.0	7	1	white	0.12	1.0	1.0	1.0
39	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	0.12	1.0	1.0	1.0
40	7.8	0.26	0.49	3.2	0.027	28.0	87.0	0.9919	3.03	0.32	11.3	7	1	white	0.1	1.0	1.0	1.0
41	7.0	0.14	0.49	5.9	0.053	22.0	118.0	0.9954	3.36	0.36	9.4	6	0	white	0.03	1.0	1.0	1.0
42	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	0.02	1.0	1.0	1.0
43	7.5	0.14	0.74	1.6	0.035	21.0	126.0	0.9933	3.26	0.45	10.2	6	0	white	0.16	1.0	1.0	1.0
44	8.1	0.12	0.49	1.2	0.042	43.0	160.0	0.9934	3.13	0.48	9.7	6	0	white	0.11	1.0	1.0	1.0
45	7.5	0.24	0.49	9.4	0.048	50.0	149.0	0.9962	3.17	0.59	10.5	7	1	white	0.11	1.0	1.0	1.0
46	7.8	0.21	0.49	1.2	0.036	20.0	99.0	0.99	3.05	0.28	12.1	7	1	white	0.02	1.0	1.0	1.0
47	7.1	0.3	0.49	1.6	0.045	31.0	100.0	0.9942	3.4	0.59	10.2	5	0	white	0.08	1.0	1.0	1.0
48	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	0.17	1.0	1.0	1.0
49	6.7	0.21	0.49	1.4	0.047	30.0	114.0	0.9914	2.92	0.42	10.8	7	1	white	0.16	1.0	1.0	1.0
50	7.9	0.18	0.33	1.2	0.033	20.0	72.0	0.9922	3.12	0.38	10.5	7	1	white	0.04	1.0	1.0	1.0
51	7.8	0.24	0.18	6.7	0.046	33.0	160.0	0.9963	3.2	0.56	9.8	6	0	white	0.11	1.0	1.0	1.0
52	6.8	0.22	0.29	8.9	0.046	82.0	188.0	0.9955	3.3	0.44	10.3	6	0	white	0.14	1.0	1.0	1.0
53	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	0.14	1.0	1.0	1.0
54	6.7	0.25	0.26	1.55	0.041	118.5	216.0	0.9949	3.55	0.63	9.4	3	0	white	0.1	1.0	1.0	1.0
55	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	0.06	1.0	1.0	1.0
56	7.7	0.12	0.32	1.4	0.06	47.0	150.0	0.9952	3.37	0.42	9.2	6	0	white	0.13	1.0	1.0	1.0
57	7.2	0.21	0.33	3.0	0.036	35.0	132.0	0.9928	3.25	0.4	11.0	6	0	white	0.07	1.0	1.0	1.0
58	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	0.08	1.0	1.0	1.0
59	7.0	0.24	0.3	4.2	0.04	41.0	213.0	0.9927	3.28	0.49	11.8	6	0	white	0.0	1.0	1.0	1.0
60	7.9	0.29	0.36	11.1	0.033	43.0	208.0	0.9969	3.14	0.46	10.3	5	0	white	0.17	1.0	1.0	1.0
61	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	0.02	1.0	1.0	1.0
62	7.0	0.61	0.26	1.7	0.051	25.0	161.0	0.9946	3.36	0.6	10.6	4	0	white	0.15	1.0	1.0	1.0
63	6.8	0.22	0.3	13.6	0.055	50.0	180.0	0.9984	3.44	0.39	9.8	5	0	white	0.14	1.0	1.0	1.0
64	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	0.02	1.0	1.0	1.0
65	7.6	0.2	0.39	2.6	0.044	30.0	180.0	0.9941	3.46	0.44	10.8	7	1	white	0.14	1.0	1.0	1.0
66	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	0.17	1.0	1.0	1.0
67	9.2	0.23	0.3	1.1	0.031	40.0	99.0	0.9929	2.94	0.3	10.4	6	0	white	0.0	1.0	1.0	1.0
68	7.5	0.15	0.38	1.8	0.054	19.0	101.0	0.9946	3.24	0.44	10.0	5	0	white	0.0	1.0	1.0	1.0
69	7.7	0.38	0.4	2.0	0.038	28.0	152.0	0.9906	3.18	0.32	12.9	6	0	white	0.06	1.0	1.0	1.0
70	8.3	0.3	0.36	10.0	0.042	33.0	169.0	0.9982	3.23	0.51	9.3	6	0	white	0.16	1.0	1.0	1.0
71	6.1	0.17	0.27	1.5	0.056	45.0	135.0	0.9924	3.2	0.43	10.2	6	0	white	0.04	1.0	1.0	1.0
72	6.4	0.16	0.28	2.2	0.042	33.0	93.0	0.9914	3.31	0.43	11.1	6	0	white	0.18	1.0	1.0	1.0
73	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	0.05	1.0	1.0	1.0
74	6.9	0.29	0.23	8.6	0.056	56.0	215.0	0.9967	3.17	0.44	8.8	5	0	white	0.06	1.0	1.0	1.0
75	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	0.12	1.0	1.0	1.0
76	6.6	0.25	0.35	14.0	0.069	42.0	163.0	0.999	3.56	0.47	9.8	5	0	white	0.19	1.0	1.0	1.0
77	6.5	0.18	0.26	1.4	0.041	40.0	141.0	0.9941	3.34	0.72	9.5	6	0	white	0.05	1.0	1.0	1.0
78	8.5	0.21	0.26	9.25	0.034	73.0	142.0	0.9945	3.05	0.37	11.4	6	0	white	0.13	1.0	1.0	1.0
79	8.5	0.21	0.26	9.25	0.034	73.0	142.0	0.9945	3.05	0.37	11.4	6	0	white	0.08	1.0	1.0	1.0
80	6.6	0.37	0.24	2.0	0.064	23.0	120.0	0.9946	3.32	0.54	9.4	5	0	white	0.04	1.0	1.0	1.0
81	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	0.12	1.0	1.0	1.0
82	6.8	0.23	0.29	15.4	0.073	56.0	173.0	0.9984	3.06	0.41	8.7	6	0	white	0.17	1.0	1.0	1.0
83	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	0.19	1.0	1.0	1.0
84	6.6	0.36	0.21	1.5	0.049	39.0	184.0	0.9928	3.18	0.41	9.9	6	0	white	0.12	1.0	1.0	1.0
85	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	0.18	1.0	1.0	1.0
86	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	0.07	1.0	1.0	1.0
87	7.7	0.44	0.24	11.2	0.031	41.0	167.0	0.9948	3.12	0.43	11.3	7	1	white	0.04	1.0	1.0	1.0
88	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	0.01	1.0	1.0	1.0
89	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	0.14	1.0	1.0	1.0
90	6.1	0.34	0.46	4.7	0.029	21.0	94.0	0.991	3.29	0.62	12.3	6	0	white	0.1	1.0	0.6	0.6
91	8.3	0.27	0.39	2.4	0.058	16.0	107.0	0.9955	3.28	0.59	10.3	5	0	white	0.17	1.0	1.0	1.0
92	8.9	0.33	0.34	1.4	0.056	14.0	171.0	0.9946	3.13	0.47	9.7	5	0	white	0.07	1.0	1.0	1.0
93	7.8	0.2	0.28	10.2	0.054	78.0	186.0	0.997	3.14	0.46	10.0	6	0	white	0.14	1.0	1.0	1.0
94	8.0	0.19	0.3	2.0	0.053	48.0	140.0	0.994	3.18	0.49	9.6	6	0	white	0.05	1.0	1.0	1.0
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	0.17	1.0	1.0	1.0
96	7.7	0.3	0.42	14.3	0.045	45.0	213.0	0.9991	3.18	0.63	9.2	5	0	white	0.1	1.0	1.0	1.0
97	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	0.14	1.0	1.0	1.0
98	7.0	0.53	0.02	1.0	0.036	39.0	107.0	0.993	3.2	0.32	9.0	5	0	white	0.0	1.0	1.0	1.0
99	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	0.13	1.0	1.0	1.0
100	7.6	0.33	0.41	13.7	0.045	44.0	197.0	0.9989	3.18	0.64	9.1	5	0	white	0.13	1.0	1.0	1.0

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¶

Tree models can be visualized by drawing their tree plots. For more examples, check out Machine Learning - Tree Plots.

model.plot_tree()

../_images/machine_learning_VAST_tree_rf_classifier_.png

Note

The above example may not render properly in the doc because of the huge size of the tree. But it should render nicely in jupyter environment.

In order to plot graph using graphviz separately, you can extract the graphviz DOT file code as follows:

model.to_graphviz()

This string can then be copied into a DOT file which can beparsed by graphviz.

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({'max_depth': 5})

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, ...])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`confusion_matrix`([pos_label, cutoff])	Computes the model confusion matrix.
`contour`([pos_label, nbins, chart])	Draws the model's contour plot.
`cutoff_curve`([pos_label, nbins, show, chart])	Draws the model Cutoff curve.
`deploySQL`([X, pos_label, cutoff, allSQL])	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`([tree_id, 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.
`get_tree`([tree_id])	Returns a table with all the input tree information.
`import_models`(path[, schema, kind])	Imports machine learning models.
`lift_chart`([pos_label, nbins, show, chart])	Draws the model Lift Chart.
`plot`([max_nb_points, chart])	Draws the model.
`plot_tree`([tree_id, pic_path])	Draws the input tree.
`prc_curve`([pos_label, nbins, show, chart])	Draws the model PRC curve.
`predict`(vdf[, X, name, cutoff, inplace])	Predicts using the input relation.
`predict_proba`(vdf[, X, name, pos_label, inplace])	Returns the model's probabilities using the input relation.
`report`([metrics, cutoff, labels, nbins])	Computes a classification report using multiple model evaluation metrics (`auc`, `accuracy`, `f1`...).
`roc_curve`([pos_label, nbins, show, chart])	Draws the model ROC curve.
`score`([metric, average, pos_label, cutoff, ...])	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_graphviz`([tree_id, classes_color, ...])	Returns the code for a Graphviz tree.
`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

object_type