vastorbit.machine_learning.vast.neighbors.KNeighborsClassifier¶

class vastorbit.machine_learning.vast.neighbors.KNeighborsClassifier(name: str = None, overwrite_model: bool = False, n_neighbors: int = 5, p: int = 2)¶

[Beta Version] Creates a KNeighborsClassifier object using the k-nearest neighbors algorithm. This object uses pure SQL to compute all the distances and final score.

Warning

This algorithm uses a CROSS JOIN during computation and is therefore computationally expensive at O(n * n), where n is the total number of elements. Since KNeighborsClassifier uses the p- distance, it is highly sensitive to unnormalized data.

Important

This algorithm is not VAST Native and relies solely on SQL for attribute computation. While this model does not take advantage of the benefits provided by a model management system, including versioning and tracking, the SQL code it generates can still be used to create a pipeline.

Parameters:

n_neighbors (int, optional) – Number of neighbors to consider when computing the score.
p (int, optional) – The p of the p-distances (distance metric used during the model computation).

Variables:

created (Many attributes are)
phase. (during the fitting)
n_neighbors_ (int) – Number of neighbors.
p_ (int) – The p of the p-distances.
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.

There are multiple classes for the “quality” column. Let us filter the data for classes between 5 and 7:

data = data[data["quality"]>=5]
data = data[data["quality"]<=7]

We can the balance the dataset to ensure equal representation:

data = data.balance(column="quality", x = 1)

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.

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 KNeighborsClassifier model:

from vastorbit.machine_learning.vast import KNeighborsClassifier

Then we can create the model:

model = KNeighborsClassifier(
   n_neighbors = 10,
   p = 2,
)

Model Training¶

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "density",
        "pH",
    ],
    "quality",
    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.

Important

As this model is not native, it solely relies on SQL statements to compute various attributes, storing them within the object. No data is saved in the database.

Metrics¶

We can get the entire report using:

model.report()

	5	6	7	avg_macro	avg_weighted	avg_micro
auc	0	0	0	0.0	0.0	[null]
prc_auc	0.5	0.5	0.6658841940532081	0.5552947313510693	0.5550351316733648	[null]
accuracy	0.6885758998435054	0.6431924882629108	0.3317683881064163	0.5545122587376108	0.554005304650018	0.5545122587376109
log_loss	[null]	[null]	138.47941333921926	138.47941333921926	45.94309174947493	[null]
precision	0.0	0.0	0.3317683881064163	0.11058946270213876	0.11007026334672967	0.3317683881064163
recall	0.0	0.0	1.0	0.3333333333333333	0.3317683881064163	0.3317683881064163
f1_score	0.0	0.0	0.49823736780258526	0.16607912260086174	0.16529940841024737	0.3317683881064163
mcc	0.0	0.0	0.0	0.0	0.0	-0.002347417840375587
informedness	0.0	0.0	0.0	0.0	0.0	-0.0023474178403756207
markedness	-0.3114241001564946	-0.35680751173708924	-0.6682316118935837	-0.4454877412623892	-0.44599469534998204	-0.0023474178403756207
csi	0.0	0.0	0.3317683881064163	0.11058946270213876	0.11007026334672967	0.19887429643527205

Rows: 1-11 | Columns: 7

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)

	5	6	7	avg_macro	avg_weighted	avg_micro
auc	0	0	0	0.0	0.0	[null]
prc_auc	0.5	0.5	0.6658841940532081	0.5552947313510693	0.6658841940532081	[null]
accuracy	nan	nan	0.3317683881064163	0.3317683881064163	0.33176838810641635	0.3317683881064163
log_loss	[null]	[null]	138.4794133392193	138.4794133392193	138.4794133392193	[null]
precision	0.0	0.0	0.3317683881064163	0.11058946270213876	0.33176838810641635	0.3317683881064163
recall	0.0	0.0	1.0	0.3333333333333333	1.0	1.0
f1_score	0.0	0.0	0.49823736780258526	0.16607912260086174	0.4982373678025852	0.49823736780258526
mcc	0.0	0.0	0.0	0.0	0.0	0.0
informedness	-1.0	-1.0	0.0	-0.6666666666666666	0.0	0.0
markedness	-1.0	-1.0	-0.6682316118935837	-0.8894105372978612	-0.6682316118935837	-0.6682316118935837
csi	0.0	0.0	0.3317683881064163	0.11058946270213876	0.33176838810641635	0.3317683881064163

Rows: 1-11 | Columns: 7

You can also use the KNeighborsClassifier.score function to compute any classification metric. The default metric is the accuracy:

model.score(metric = "f1", average = "macro")

Note

For multi-class scoring, vastorbit allows the flexibility to use three averaging techniques: micro, macro and weighted. Please refer to this link for more details on how they are calculated.

Prediction¶

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "density",
        "pH",
    ],
    "prediction",
)

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 density Double	123 pH Decimal(6, 3)	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 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	0.28	0.9958	3.39	0.24	8.45	0.031	32.0	172.0	0.57	9.7	7	1	white	0.01	7
2	6.6	0.58	0.99374	3.35	0.02	2.0	0.062	37.0	53.0	0.76	11.6	7	1	red	0.13	7
3	5.6	0.235	0.991	3.34	0.29	1.2	0.047	33.0	127.0	0.5	11.0	7	1	white	0.09	7
4	5.9	0.34	0.9894	3.41	0.22	2.4	0.03	19.0	135.0	0.78	13.9	7	1	white	0.02	7
5	6.5	0.29	0.99053	3.27	0.31	1.7	0.035	24.0	79.0	0.69	11.4	7	1	white	0.07	7
6	5.8	0.28	0.98952	3.32	0.3	1.5	0.026	31.0	114.0	0.6	12.5	7	1	white	0.09	7
7	6.8	0.21	0.99214	3.18	0.4	6.3	0.032	40.0	121.0	0.53	12.0	7	1	white	0.06	7
8	7.7	0.27	0.9972	3.25	0.68	3.5	0.358	5.0	10.0	1.08	9.9	7	1	red	0.11	7
9	9.8	0.34	0.9947	3.19	0.39	1.4	0.066	3.0	7.0	0.55	11.4	7	1	red	0.07	7
10	7.0	0.32	0.99165	3.15	0.27	7.1	0.027	37.0	122.0	0.6	12.6	7	1	white	0.1	7
11	7.4	0.16	0.99198	3.04	0.33	1.2	0.042	47.0	121.0	0.68	10.5	7	1	white	0.18	7
12	6.6	0.29	0.99685	3.02	0.44	9.0	0.053	62.0	178.0	0.45	8.9	5	0	white	0.01	7
13	6.0	0.28	0.9953	3.19	0.49	6.8	0.048	61.0	222.0	0.47	9.3	5	0	white	0.06	7
14	6.8	0.61	0.99525	3.42	0.04	1.5	0.057	5.0	10.0	0.6	9.5	5	0	red	0.01	7
15	7.6	0.42	0.99784	3.15	0.25	3.9	0.104	28.0	90.0	0.57	9.1	5	0	red	0.19	7
16	6.9	0.58	0.99683	3.4	0.01	1.9	0.08	40.0	54.0	0.73	9.7	5	0	red	0.19	7
17	6.5	0.28	1.0002	3.14	0.28	20.4	0.041	40.0	144.0	0.38	8.7	5	0	white	0.19	7
18	6.9	0.4	0.99705	3.4	0.22	5.95	0.081	76.0	303.0	0.57	9.4	5	0	white	0.09	7
19	5.5	0.14	0.9949	3.34	0.27	4.6	0.029	22.0	104.0	0.44	9.0	5	0	white	0.15	7
20	6.9	0.3	0.99827	3.11	0.21	15.7	0.056	49.0	159.0	0.48	9.0	5	0	white	0.04	7
21	6.8	0.45	0.997	3.09	0.3	11.8	0.094	23.0	97.0	0.44	9.6	5	0	white	0.03	7
22	6.0	0.39	0.99114	3.06	0.13	1.2	0.042	60.0	172.0	0.52	10.6	5	0	white	0.04	7
23	6.5	0.41	0.9946	3.14	0.22	4.8	0.052	49.0	142.0	0.62	9.2	5	0	white	0.12	7
24	6.3	0.34	0.99481	3.18	0.52	6.3	0.047	63.0	186.0	0.44	9.6	5	0	white	0.07	7
25	6.4	0.29	0.99478	3.1	0.3	6.5	0.209	62.0	156.0	0.4	9.4	5	0	white	0.15	7
26	6.2	0.25	0.99804	3.14	0.44	15.8	0.057	39.0	167.0	0.51	9.2	5	0	white	0.15	7
27	6.4	0.22	0.98958	3.18	0.34	1.4	0.023	56.0	115.0	0.7	11.7	6	0	white	0.12	7
28	6.7	0.51	0.9904	3.22	0.24	2.1	0.043	14.0	155.0	0.6	13.0	6	0	white	0.1	7
29	8.1	0.19	1.0006	3.2	0.58	16.65	0.049	48.0	181.0	0.62	9.1	6	0	white	0.07	7
30	8.0	0.36	0.9982	3.4	0.43	10.1	0.053	29.0	146.0	0.46	9.5	6	0	white	0.09	7
31	6.7	0.45	0.99122	3.12	0.3	5.3	0.036	27.0	165.0	0.46	12.2	6	0	white	0.05	7
32	5.8	0.345	0.99494	3.25	0.15	10.8	0.033	26.0	120.0	0.49	10.0	6	0	white	0.15	7
33	6.6	0.28	0.99566	2.93	0.09	10.9	0.051	37.0	131.0	0.62	9.5	6	0	white	0.14	7
34	7.7	0.58	0.99565	3.08	0.1	1.8	0.102	28.0	109.0	0.49	9.8	6	0	red	0.02	7
35	10.6	0.5	0.99708	3.23	0.45	2.6	0.119	34.0	68.0	0.72	10.9	6	0	red	0.03	7
36	5.4	0.15	0.98878	3.04	0.32	2.5	0.037	10.0	51.0	0.58	12.6	6	0	white	0.04	7
37	5.8	0.2	0.99115	3.44	0.3	1.5	0.031	21.0	57.0	0.55	11.0	6	0	white	0.04	7
38	7.2	0.38	0.99685	3.42	0.3	1.8	0.073	31.0	70.0	0.59	9.5	6	0	red	0.02	7
39	7.5	0.63	0.99616	3.26	0.27	2.0	0.083	17.0	91.0	0.58	9.8	6	0	red	0.04	7
40	7.5	0.38	0.99605	3.36	0.57	2.3	0.106	5.0	12.0	0.55	11.4	6	0	red	0.04	7
41	7.6	0.29	0.9938	3.13	0.49	9.6	0.03	45.0	197.0	0.38	12.3	7	1	white	0.19	7
42	7.5	0.31	0.99397	3.16	0.24	7.1	0.031	28.0	141.0	0.38	10.6	7	1	white	0.08	7
43	7.1	0.13	0.9932	3.3	0.4	1.2	0.047	54.0	134.0	0.97	9.8	7	1	white	0.12	7
44	7.5	0.34	0.9924	3.16	0.35	6.0	0.034	12.0	126.0	0.39	12.0	7	1	white	0.02	7
45	6.7	0.15	0.9946	3.52	0.29	5.0	0.058	28.0	105.0	0.44	10.2	7	1	white	0.17	7
46	5.4	0.5	0.99079	3.48	0.13	5.0	0.028	12.0	107.0	0.88	13.5	7	1	white	0.06	7
47	6.6	0.26	0.99089	3.22	0.21	2.9	0.026	48.0	126.0	0.38	11.3	7	1	white	0.11	7
48	8.0	0.24	0.99316	3.44	0.26	1.7	0.033	36.0	136.0	0.51	10.4	7	1	white	0.06	7
49	10.2	0.29	0.9976	3.05	0.49	2.6	0.059	5.0	13.0	0.74	10.5	7	1	red	0.07	7
50	5.6	0.19	0.98964	3.46	0.31	2.7	0.027	11.0	100.0	0.4	13.2	7	1	white	0.0	7
51	6.3	0.43	0.99172	3.28	0.32	8.8	0.042	18.0	106.0	0.33	12.9	7	1	white	0.05	7
52	6.7	0.19	0.99173	2.9	0.32	3.7	0.041	26.0	76.0	0.57	10.5	7	1	white	0.03	7
53	5.4	0.22	0.99092	3.29	0.35	6.5	0.029	26.0	87.0	0.44	12.5	7	1	white	0.07	7
54	6.0	0.2	0.99051	3.31	0.33	1.1	0.039	45.0	126.0	0.45	11.6	7	1	white	0.13	7
55	7.2	0.2	0.9918	3.21	0.38	1.0	0.037	21.0	74.0	0.37	11.0	5	0	white	0.04	7
56	8.4	0.57	0.9981	3.15	0.44	10.7	0.051	46.0	195.0	0.51	10.4	5	0	white	0.0	7
57	8.5	0.66	0.99733	3.13	0.2	2.1	0.0969999999999999	23.0	113.0	0.48	9.2	5	0	red	0.04	7
58	11.2	0.4	0.99783	3.1	0.5	2.0	0.099	19.0	50.0	0.58	10.4	5	0	red	0.01	7
59	7.3	0.32	0.9985	3.2	0.23	13.7	0.05	49.0	197.0	0.46	8.7	5	0	white	0.02	7
60	7.7	0.25	0.99555	3.1	0.3	7.8	0.038	67.0	196.0	0.5	10.1	5	0	white	0.12	7
61	6.8	0.26	0.9998	3.45	0.29	16.95	0.056	48.0	179.0	0.4	9.6	5	0	white	0.17	7
62	9.6	0.21	0.9926	2.8	0.28	1.2	0.038	12.0	53.0	0.46	10.6	5	0	white	0.19	7
63	8.7	0.24	0.9926	3.08	0.35	0.6	0.042	11.0	71.0	0.38	10.6	5	0	white	0.15	7
64	7.1	0.33	0.99474	2.9	0.18	6.3	0.094	27.0	166.0	0.49	9.5	5	0	white	0.18	7
65	6.4	0.27	0.9955	3.18	0.45	8.3	0.05	52.0	196.0	0.48	9.5	5	0	white	0.03	7
66	6.4	0.32	0.9986	3.26	0.23	16.2	0.055	36.0	176.0	0.54	9.1	5	0	white	0.17	7
67	6.2	0.25	0.99586	3.16	0.42	8.0	0.049	53.0	206.0	0.47	9.1	6	0	white	0.09	7
68	8.5	0.28	0.9981	3.13	0.34	13.8	0.041	32.0	161.0	0.4	9.9	6	0	white	0.1	7
69	6.8	0.38	0.99326	3.11	0.29	9.9	0.037	40.0	146.0	0.37	11.5	6	0	white	0.08	7
70	5.1	0.3	0.98944	3.29	0.3	2.3	0.048	40.0	150.0	0.46	12.2	6	0	white	0.13	7
71	11.6	0.44	0.998	3.21	0.64	2.1	0.059	5.0	15.0	0.67	10.2	6	0	red	0.06	7
72	11.5	0.18	0.9996	3.28	0.51	4.0	0.104	4.0	23.0	0.97	10.1	6	0	red	0.01	7
73	13.4	0.27	1.0002	3.16	0.62	2.6	0.0819999999999999	6.0	21.0	0.67	9.7	6	0	red	0.07	7
74	6.0	0.22	0.99261	2.94	0.23	5.0	0.045	10.0	122.0	0.63	10.0	6	0	white	0.07	7
75	5.7	0.22	0.99862	3.22	0.2	16.0	0.044	41.0	113.0	0.46	8.9	6	0	white	0.01	7
76	7.3	0.19	0.9946	3.32	0.68	1.5	0.05	31.0	156.0	0.64	9.4	6	0	white	0.18	7
77	6.9	0.24	0.9935	3.2	0.34	4.7	0.04	43.0	161.0	0.59	10.6	6	0	white	0.01	7
78	6.0	0.24	0.9938	3.64	0.27	1.9	0.048	40.0	170.0	0.54	10.0	7	1	white	0.13	7
79	7.2	0.2	0.99	3.16	0.34	2.7	0.032	49.0	151.0	0.39	12.7	7	1	white	0.11	7
80	7.3	0.18	0.9918	3.2	0.29	1.2	0.044	12.0	143.0	0.48	11.3	7	1	white	0.17	7
81	5.0	0.27	0.98956	3.45	0.32	4.5	0.032	58.0	178.0	0.31	12.6	7	1	white	0.17	7
82	8.9	0.4	0.995	3.32	0.51	2.6	0.052	13.0	27.0	0.9	13.4	7	1	red	0.01	7
83	8.7	0.48	0.9964	3.33	0.3	2.8	0.066	10.0	28.0	0.67	11.2	7	1	red	0.08	7
84	9.1	0.21	0.99552	3.23	0.37	1.6	0.067	6.0	10.0	0.58	11.1	7	1	red	0.07	7
85	8.9	0.3	0.99458	3.15	0.35	4.6	0.032	32.0	148.0	0.45	11.5	7	1	white	0.04	7
86	8.7	0.3	0.99474	3.12	0.34	4.8	0.018	23.0	127.0	0.49	11.2	7	1	white	0.14	7
87	7.5	0.34	0.98958	3.2	0.28	4.0	0.028	46.0	100.0	0.5	13.2	7	1	white	0.16	7
88	7.4	0.16	0.99825	2.9	0.3	13.7	0.056	33.0	168.0	0.44	8.7	7	1	white	0.09	7
89	5.8	0.415	0.9922	3.29	0.13	1.4	0.04	11.0	64.0	0.52	10.5	5	0	white	0.15	7
90	6.4	0.5	0.9974	3.28	0.16	12.9	0.042	26.0	138.0	0.33	9.0	5	0	white	0.06	7
91	7.0	0.62	0.9978	3.48	0.08	1.8	0.076	8.0	24.0	0.53	9.0	5	0	red	0.0	7
92	9.9	0.5	1.00242	3.16	0.5	13.8	0.205	48.0	82.0	0.75	8.8	5	0	red	0.01	7
93	8.1	0.27	0.9908	2.99	0.41	1.45	0.033	11.0	63.0	0.56	12.0	5	0	white	0.06	7
94	7.5	0.27	0.9939	3.03	0.36	7.0	0.036	45.0	164.0	0.33	11.0	5	0	white	0.17	7
95	8.6	0.18	0.99104	2.99	0.28	0.8	0.032	25.0	78.0	0.38	11.1	5	0	white	0.03	7
96	6.1	0.22	0.9928	3.3	0.49	1.5	0.051	18.0	87.0	0.46	9.6	5	0	white	0.05	7
97	7.8	0.27	0.9934	3.15	0.28	1.8	0.05	21.0	127.0	0.44	9.9	5	0	white	0.11	7
98	7.7	0.26	0.993	3.21	0.32	1.2	0.04	26.0	117.0	0.56	10.8	5	0	white	0.05	7
99	6.9	0.32	0.996	3.23	0.13	7.8	0.042	11.0	117.0	0.37	9.2	5	0	white	0.12	7
100	7.1	0.26	0.996	3.16	0.19	8.2	0.051	53.0	187.0	0.52	9.7	5	0	white	0.18	7

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",
        "density",
        "pH",
    ],
    "prediction",
)

	123 fixed_acidity Decimal(6, 3)	123 volatile_acidity Decimal(7, 4)	123 density Double	123 pH Decimal(6, 3)	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 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_7 Double
1	7.8	0.28	0.9901	3.0	0.34	1.6	0.028	32.0	118.0	0.38	12.1	7	1	white	0.13	7	1.0
2	8.0	0.57	0.9917	3.29	0.39	3.9	0.034	22.0	122.0	0.67	12.8	7	1	white	0.0	7	1.0
3	7.3	0.65	0.9946	3.39	0.0	1.2	0.065	15.0	21.0	0.47	10.0	7	1	red	0.05	7	1.0
4	7.9	0.65	0.9963	3.34	0.01	2.5	0.078	17.0	38.0	0.74	11.7	7	1	red	0.19	7	1.0
5	10.5	0.42	0.997	3.24	0.66	2.95	0.1159999999999999	12.0	29.0	0.75	11.7	7	1	red	0.15	7	1.0
6	10.2	0.44	0.99745	3.29	0.58	4.1	0.092	11.0	24.0	0.99	12.0	7	1	red	0.09	7	1.0
7	5.3	0.3	0.98742	3.31	0.3	1.2	0.029	25.0	93.0	0.4	13.6	7	1	white	0.08	7	1.0
8	6.8	0.18	0.99406	3.11	0.28	9.8	0.039	29.0	113.0	0.45	10.9	7	1	white	0.11	7	1.0
9	7.1	0.33	0.9912	3.08	0.3	3.3	0.034	30.0	102.0	0.31	12.3	7	1	white	0.0	7	1.0
10	7.4	0.24	0.9936	3.18	0.26	1.6	0.058	53.0	150.0	0.5	9.9	7	1	white	0.01	7	1.0
11	6.0	0.38	0.98872	3.18	0.26	3.5	0.035	38.0	111.0	0.47	13.6	7	1	white	0.16	7	1.0
12	5.3	0.47	0.99182	3.54	0.11	2.2	0.048	16.0	89.0	0.88	13.6	7	1	red	0.12	7	1.0
13	11.9	0.38	0.9996	3.24	0.51	2.0	0.121	7.0	20.0	0.76	10.4	6	0	red	0.12	7	1.0
14	9.8	0.25	0.9982	3.42	0.49	2.7	0.088	15.0	33.0	0.9	10.0	6	0	red	0.01	7	1.0
15	5.8	0.13	0.9956	3.32	0.22	12.7	0.058	24.0	183.0	0.42	11.7	6	0	white	0.01	7	1.0
16	7.0	0.26	0.9986	2.94	0.46	15.55	0.037	61.0	171.0	0.35	8.8	6	0	white	0.04	7	1.0
17	6.6	0.25	0.9942	3.37	0.24	1.7	0.048	26.0	124.0	0.6	10.1	6	0	white	0.16	7	1.0
18	7.6	0.23	0.9967	3.08	0.49	10.0	0.036	45.0	182.0	0.58	9.6	6	0	white	0.08	7	1.0
19	6.4	0.37	0.9982	3.14	0.49	13.3	0.045	53.0	243.0	0.48	8.5	6	0	white	0.12	7	1.0
20	7.3	0.21	0.997	3.4	0.3	10.9	0.037	18.0	112.0	0.5	9.6	6	0	white	0.01	7	1.0
21	7.3	0.19	0.9964	3.36	0.24	6.3	0.054	34.0	231.0	0.54	10.0	6	0	white	0.18	7	1.0
22	6.8	0.14	0.99164	3.27	0.18	1.4	0.047	30.0	90.0	0.54	11.2	6	0	white	0.17	7	1.0
23	7.0	0.24	0.99636	3.47	0.24	9.0	0.03	42.0	219.0	0.46	10.2	6	0	white	0.14	7	1.0
24	6.7	0.2	0.99104	3.11	0.3	1.4	0.025	17.0	76.0	0.44	11.0	6	0	white	0.09	7	1.0
25	6.5	0.2	0.99941	2.94	0.5	18.1	0.054	50.0	221.0	0.64	8.8	6	0	white	0.03	7	1.0
26	6.4	0.16	0.9908	3.18	0.42	1.0	0.036	29.0	113.0	0.52	11.0	6	0	white	0.05	7	1.0
27	6.6	0.18	0.99207	3.13	0.28	1.7	0.041	53.0	161.0	0.45	10.2	6	0	white	0.07	7	1.0
28	6.4	0.32	0.99	3.06	0.31	1.9	0.037	34.0	126.0	0.45	11.8	6	0	white	0.01	7	1.0
29	7.1	0.6	0.99514	3.4	0.01	2.3	0.079	24.0	37.0	0.61	10.9	6	0	red	0.03	7	1.0
30	6.2	0.57	0.99448	3.44	0.1	2.1	0.048	4.0	11.0	0.76	10.8	6	0	red	0.13	7	1.0
31	6.0	0.19	0.9972	3.3	0.26	12.4	0.048	50.0	147.0	0.36	8.9	6	0	white	0.09	7	1.0
32	5.7	0.36	0.9941	3.29	0.21	6.7	0.038	51.0	166.0	0.63	10.0	6	0	white	0.18	7	1.0
33	6.8	0.39	0.99775	3.07	0.35	11.6	0.044	57.0	220.0	0.53	9.3	5	0	white	0.0	7	1.0
34	10.0	0.73	0.9966	3.15	0.43	2.3	0.059	15.0	31.0	0.57	11.0	5	0	red	0.03	7	1.0
35	9.0	0.46	0.9983	3.1	0.23	2.8	0.092	28.0	104.0	0.56	9.2	5	0	red	0.07	7	1.0
36	7.5	0.42	0.99628	3.24	0.32	2.7	0.067	7.0	25.0	0.44	10.4	5	0	red	0.04	7	1.0
37	6.1	0.46	0.99537	3.35	0.32	6.2	0.053	10.0	94.0	0.47	10.1	5	0	white	0.11	7	1.0
38	7.4	0.74	0.99502	3.12	0.07	1.7	0.086	15.0	48.0	0.48	10.0	5	0	red	0.15	7	1.0
39	7.3	0.28	0.9994	3.28	0.42	14.4	0.04	49.0	173.0	0.82	9.0	7	1	white	0.12	7	1.0
40	6.9	0.28	0.9918	3.35	0.3	1.6	0.047	46.0	132.0	0.38	11.1	7	1	white	0.13	7	1.0
41	6.6	0.32	0.99198	3.4	0.26	4.6	0.031	26.0	120.0	0.73	12.5	7	1	white	0.17	7	1.0
42	6.0	0.31	0.98952	3.32	0.27	2.3	0.042	19.0	120.0	0.41	12.7	7	1	white	0.09	7	1.0
43	7.0	0.26	0.99787	3.47	0.26	10.8	0.039	37.0	184.0	0.58	10.3	7	1	white	0.11	7	1.0
44	6.8	0.18	0.99808	3.0	0.3	12.8	0.062	19.0	171.0	0.52	9.0	7	1	white	0.07	7	1.0
45	6.7	0.3	0.99159	3.31	0.29	2.8	0.025	37.0	107.0	0.63	11.3	7	1	white	0.0	7	1.0
46	6.7	0.28	0.99064	3.26	0.28	2.4	0.012	36.0	100.0	0.39	11.7	7	1	white	0.08	7	1.0
47	5.8	0.22	0.98936	3.14	0.29	0.9	0.034	34.0	89.0	0.36	11.1	7	1	white	0.06	7	1.0
48	5.9	0.33	0.9911	3.22	0.32	8.1	0.038	9.0	34.0	0.36	12.7	7	1	white	0.14	7	1.0
49	6.4	0.23	0.99216	3.18	0.35	4.6	0.039	43.0	147.0	0.4	11.0	7	1	white	0.19	7	1.0
50	6.1	0.32	0.9898	3.29	0.24	1.5	0.036	38.0	124.0	0.42	12.4	7	1	white	0.02	7	1.0
51	12.9	0.5	1.00012	3.09	0.55	2.8	0.072	7.0	24.0	0.68	10.9	6	0	red	0.07	7	1.0
52	10.4	0.26	0.99724	3.33	0.48	1.9	0.066	6.0	10.0	0.87	10.9	6	0	red	0.0	7	1.0
53	6.5	0.29	0.99339	3.24	0.3	9.15	0.051	25.0	166.0	0.56	11.35	6	0	white	0.04	7	1.0
54	6.7	0.2	0.9987	3.16	0.42	14.0	0.038	83.0	160.0	0.5	9.4	6	0	white	0.08	7	1.0
55	6.8	0.18	0.992	3.3	0.37	1.5	0.027	37.0	93.0	0.45	10.8	6	0	white	0.03	7	1.0
56	6.5	0.22	0.99297	3.05	0.19	1.1	0.064	36.0	191.0	0.5	9.5	6	0	white	0.05	7	1.0
57	7.2	0.24	0.99076	3.13	0.36	2.0	0.029	21.0	63.0	0.63	12.5	6	0	white	0.04	7	1.0
58	6.2	0.24	0.9952	3.19	0.28	12.2	0.049	54.0	133.0	0.37	10.7	6	0	white	0.13	7	1.0
59	7.3	0.26	0.99693	3.1	0.33	11.8	0.057	48.0	127.0	0.55	10.0	6	0	white	0.02	7	1.0
60	7.8	0.3	0.99222	3.06	0.36	4.6	0.024	20.0	198.0	0.66	11.9	6	0	white	0.07	7	1.0
61	6.3	0.2	0.9917	3.46	0.4	1.5	0.037	35.0	107.0	0.5	11.4	6	0	white	0.15	7	1.0
62	7.3	0.51	0.9944	3.01	0.26	3.3	0.09	7.0	135.0	0.52	8.8	5	0	white	0.17	7	1.0
63	6.7	0.58	0.9959	3.28	0.08	1.8	0.0969999999999999	15.0	65.0	0.54	9.2	5	0	red	0.08	7	1.0
64	8.1	0.67	0.9968	3.17	0.55	1.8	0.1169999999999999	32.0	141.0	0.62	9.4	5	0	red	0.01	7	1.0
65	11.9	0.38	1.0004	3.16	0.49	2.7	0.098	12.0	42.0	0.61	10.3	5	0	red	0.17	7	1.0
66	7.1	0.31	0.9965	2.94	0.3	2.2	0.053	36.0	127.0	1.62	9.5	5	0	red	0.14	7	1.0
67	7.1	0.52	0.99745	3.5	0.03	2.6	0.076	21.0	92.0	0.6	9.8	5	0	red	0.04	7	1.0
68	6.4	0.18	0.9978	3.58	0.74	11.9	0.046	54.0	168.0	0.68	10.1	5	0	white	0.04	7	1.0
69	7.1	0.3	0.9942	3.4	0.49	1.6	0.045	31.0	100.0	0.59	10.2	5	0	white	0.1	7	1.0
70	6.4	0.22	0.9948	3.4	0.32	7.9	0.029	34.0	124.0	0.39	10.2	5	0	white	0.06	7	1.0
71	6.6	0.45	0.9954	3.12	0.43	7.2	0.064	31.0	186.0	0.44	9.4	5	0	white	0.13	7	1.0
72	7.4	0.35	0.99304	3.14	0.24	6.0	0.042	28.0	123.0	0.44	11.3	5	0	white	0.05	7	1.0
73	7.3	0.32	0.99588	3.43	0.23	2.3	0.066	35.0	70.0	0.62	10.1	5	0	red	0.08	7	1.0
74	7.1	0.31	0.99663	3.14	0.25	11.2	0.048	32.0	136.0	0.4	9.5	5	0	white	0.15	7	1.0
75	7.3	0.18	0.9918	3.2	0.29	1.2	0.044	12.0	143.0	0.48	11.3	7	1	white	0.02	7	1.0
76	7.5	0.52	0.9968	3.38	0.16	1.9	0.085	12.0	35.0	0.62	9.5	7	1	red	0.03	7	1.0
77	8.7	0.3	0.99474	3.12	0.34	4.8	0.018	23.0	127.0	0.49	11.2	7	1	white	0.01	7	1.0
78	7.4	0.16	0.9984	2.9	0.27	15.5	0.05	25.0	135.0	0.43	8.7	7	1	white	0.03	7	1.0
79	6.9	0.33	0.9924	3.25	0.26	5.0	0.027	46.0	143.0	0.43	11.2	7	1	white	0.18	7	1.0
80	6.3	0.37	0.9921	3.29	0.28	6.3	0.034	45.0	152.0	0.46	11.6	7	1	white	0.06	7	1.0
81	6.0	0.24	0.99014	3.13	0.33	2.5	0.026	31.0	85.0	0.5	11.3	7	1	white	0.07	7	1.0
82	5.8	0.17	0.99202	3.43	0.36	1.3	0.036	11.0	70.0	0.68	10.4	7	1	white	0.06	7	1.0
83	8.2	0.42	0.9911	3.0	0.29	4.1	0.03	31.0	100.0	0.32	12.8	7	1	white	0.1	7	1.0
84	7.0	0.2	0.9922	3.25	0.34	2.1	0.049	12.0	136.0	0.46	11.6	7	1	white	0.04	7	1.0
85	6.1	0.36	0.9917	3.4	0.27	2.1	0.035	16.0	100.0	0.71	11.5	7	1	white	0.13	7	1.0
86	12.7	0.59	1.0	3.0	0.45	2.3	0.0819999999999999	11.0	22.0	0.7	9.3	6	0	red	0.06	7	1.0
87	5.6	0.295	0.99154	3.47	0.26	1.1	0.035	40.0	102.0	0.56	10.6	6	0	white	0.18	7	1.0
88	6.4	0.26	0.9956	3.39	0.21	7.1	0.04	35.0	162.0	0.58	9.9	6	0	white	0.07	7	1.0
89	6.0	0.26	0.9896	3.38	0.29	1.0	0.032	27.0	96.0	0.44	12.3	6	0	white	0.01	7	1.0
90	7.2	0.2	0.997	3.4	0.31	10.0	0.054	49.0	165.0	0.42	9.9	6	0	white	0.06	7	1.0
91	7.5	0.25	0.99563	3.12	0.32	8.2	0.024	53.0	209.0	0.46	10.8	6	0	white	0.02	7	1.0
92	6.3	0.2	0.99668	3.16	0.19	12.3	0.048	54.0	145.0	0.42	9.3	6	0	white	0.08	7	1.0
93	6.7	0.39	0.9948	3.46	0.31	2.7	0.054	27.0	202.0	0.57	10.5	6	0	white	0.11	7	1.0
94	7.0	0.31	0.9954	3.13	0.26	7.4	0.069	28.0	160.0	0.46	9.8	6	0	white	0.19	7	1.0
95	6.1	0.4	0.993	3.22	0.31	0.9	0.048	23.0	170.0	0.77	9.5	6	0	white	0.17	7	1.0
96	6.3	0.34	0.9977	3.23	0.28	14.7	0.047	49.0	198.0	0.46	9.5	5	0	white	0.14	7	1.0
97	6.2	0.31	0.9941	3.15	0.21	6.3	0.041	50.0	218.0	0.6	10.0	5	0	white	0.09	7	1.0
98	6.1	0.27	0.99985	3.4	0.3	16.7	0.039	49.0	172.0	0.45	9.4	5	0	white	0.05	7	1.0
99	5.8	0.415	0.9922	3.29	0.13	1.4	0.04	11.0	64.0	0.52	10.5	5	0	white	0.14	7	1.0
100	6.8	0.24	1.0	3.3	0.31	18.3	0.046	40.0	142.0	0.41	8.7	5	0	white	0.07	7	1.0

Rows: 1-100 | Columns: 19

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)

Hint

In the context of multi-class classification, you typically work with an overall confusion matrix that summarizes the classification efficiency across all classes. However, you have the flexibility to specify a pos_label and adjust the cutoff threshold. In this case, a binary confusion matrix is computed, where the chosen class is treated as the positive class, allowing you to evaluate its efficiency as if it were a binary classification problem.

model.confusion_matrix(pos_label = "5", cutoff = 0.6)

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(pos_label = "5")

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¶

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour(pos_label = "5")

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({'n_neighbors': 8})

Model Register¶

As this model is not native, it does not support model management and versioning. However, it is possible to use the SQL code it generates for deployment.

Model Exporting¶

It is not possible to export this type of model, but you can still examine the SQL code generated by using the deploySQL() method.

__init__(name: str = None, overwrite_model: bool = False, n_neighbors: int = 5, p: int = 2) → None¶

Methods

`__init__`([name, overwrite_model, n_neighbors, p])
`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, test_relation, predict, ...])	Returns the SQL code needed to deploy the model.
`drop`()	`KNeighborsClassifier` models are not stored in the VAST DataBase.
`export_models`(name, path[, kind])	Exports machine learning models.
`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`([pos_label, nbins, show, chart])	Draws the model Lift Chart.
`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_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