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verticapy.machine_learning.vertica.neighbors.KNeighborsClassifier

class verticapy.machine_learning.vertica.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 Vertica 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).

Attributes

Many attributes are created during the fitting phase.

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 verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white
2	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
3	4.2	0.17	0.36	1.8	0.029	93.0	161.0	0.98999	3.65	0.89	12.0	7	1	white
4	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white
5	4.4	0.32	0.39	4.3	0.03	31.0	127.0	0.98904	3.46	0.36	12.8	8	1	white
6	4.4	0.46	0.1	2.8	0.024	31.0	111.0	0.98816	3.48	0.34	13.1	6	0	white
7	4.4	0.54	0.09	5.1	0.038	52.0	97.0	0.99022	3.41	0.4	12.2	7	1	white
8	4.5	0.19	0.21	0.95	0.033	89.0	159.0	0.99332	3.34	0.42	8.0	5	0	white
9	4.6	0.445	0.0	1.4	0.053	11.0	178.0	0.99426	3.79	0.55	10.2	5	0	white
10	4.6	0.52	0.15	2.1	0.054	8.0	65.0	0.9934	3.9	0.56	13.1	4	0	red
11	4.7	0.145	0.29	1.0	0.042	35.0	90.0	0.9908	3.76	0.49	11.3	6	0	white
12	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
13	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
14	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red
15	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white
16	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
17	4.8	0.13	0.32	1.2	0.042	40.0	98.0	0.9898	3.42	0.64	11.8	7	1	white
18	4.8	0.17	0.28	2.9	0.03	22.0	111.0	0.9902	3.38	0.34	11.3	7	1	white
19	4.8	0.21	0.21	10.2	0.037	17.0	112.0	0.99324	3.66	0.48	12.2	7	1	white
20	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6	0	white
21	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white
22	4.8	0.29	0.23	1.1	0.044	38.0	180.0	0.98924	3.28	0.34	11.9	6	0	white
23	4.8	0.33	0.0	6.5	0.028	34.0	163.0	0.9937	3.35	0.61	9.9	5	0	white
24	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white
25	4.8	0.65	0.12	1.1	0.013	4.0	10.0	0.99246	3.32	0.36	13.5	4	0	white
26	4.9	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
27	4.9	0.33	0.31	1.2	0.016	39.0	150.0	0.98713	3.33	0.59	14.0	8	1	white
28	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
29	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white
30	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
31	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
32	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
33	4.9	0.47	0.17	1.9	0.035	60.0	148.0	0.98964	3.27	0.35	11.5	6	0	white
34	5.0	0.17	0.56	1.5	0.026	24.0	115.0	0.9906	3.48	0.39	10.8	7	1	white
35	5.0	0.2	0.4	1.9	0.015	20.0	98.0	0.9897	3.37	0.55	12.05	6	0	white
36	5.0	0.235	0.27	11.75	0.03	34.0	118.0	0.9954	3.07	0.5	9.4	6	0	white
37	5.0	0.24	0.19	5.0	0.043	17.0	101.0	0.99438	3.67	0.57	10.0	5	0	white
38	5.0	0.24	0.21	2.2	0.039	31.0	100.0	0.99098	3.69	0.62	11.7	6	0	white
39	5.0	0.24	0.34	1.1	0.034	49.0	158.0	0.98774	3.32	0.32	13.1	7	1	white
40	5.0	0.255	0.22	2.7	0.043	46.0	153.0	0.99238	3.75	0.76	11.3	6	0	white
41	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
42	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white
43	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
44	5.0	0.29	0.54	5.7	0.035	54.0	155.0	0.98976	3.27	0.34	12.9	8	1	white
45	5.0	0.3	0.33	3.7	0.03	54.0	173.0	0.9887	3.36	0.3	13.0	7	1	white
46	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
47	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
48	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
49	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white
50	5.0	0.33	0.18	4.6	0.032	40.0	124.0	0.99114	3.18	0.4	11.0	6	0	white
51	5.0	0.33	0.23	11.8	0.03	23.0	158.0	0.99322	3.41	0.64	11.8	6	0	white
52	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
53	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white
54	5.0	0.38	0.01	1.6	0.048	26.0	60.0	0.99084	3.7	0.75	14.0	6	0	red
55	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
56	5.0	0.42	0.24	2.0	0.06	19.0	50.0	0.9917	3.72	0.74	14.0	8	1	red
57	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
58	5.0	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
59	5.0	0.55	0.14	8.3	0.032	35.0	164.0	0.9918	3.53	0.51	12.5	8	1	white
60	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white
61	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red
62	5.0	1.02	0.04	1.4	0.045	41.0	85.0	0.9938	3.75	0.48	10.5	4	0	red
63	5.0	1.04	0.24	1.6	0.05	32.0	96.0	0.9934	3.74	0.62	11.5	5	0	red
64	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
65	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
66	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
67	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white
68	5.1	0.23	0.18	1.0	0.053	13.0	99.0	0.98956	3.22	0.39	11.5	5	0	white
69	5.1	0.25	0.36	1.3	0.035	40.0	78.0	0.9891	3.23	0.64	12.1	7	1	white
70	5.1	0.26	0.33	1.1	0.027	46.0	113.0	0.98946	3.35	0.43	11.4	7	1	white
71	5.1	0.26	0.34	6.4	0.034	26.0	99.0	0.99449	3.23	0.41	9.2	6	0	white
72	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
73	5.1	0.29	0.28	8.3	0.026	27.0	107.0	0.99308	3.36	0.37	11.0	6	0	white
74	5.1	0.3	0.3	2.3	0.048	40.0	150.0	0.98944	3.29	0.46	12.2	6	0	white
75	5.1	0.305	0.13	1.75	0.036	17.0	73.0	0.99	3.4	0.51	12.3333333333333	5	0	white
76	5.1	0.31	0.3	0.9	0.037	28.0	152.0	0.992	3.54	0.56	10.1	6	0	white
77	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
78	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
79	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
80	5.1	0.33	0.27	6.7	0.022	44.0	129.0	0.99221	3.36	0.39	11.0	7	1	white
81	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
82	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
83	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white
84	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
85	5.1	0.42	0.0	1.8	0.044	18.0	88.0	0.99157	3.68	0.73	13.6	7	1	red
86	5.1	0.42	0.01	1.5	0.017	25.0	102.0	0.9894	3.38	0.36	12.3	7	1	white
87	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red
88	5.1	0.51	0.18	2.1	0.042	16.0	101.0	0.9924	3.46	0.87	12.9	7	1	red
89	5.1	0.52	0.06	2.7	0.052	30.0	79.0	0.9932	3.32	0.43	9.3	5	0	white
90	5.1	0.585	0.0	1.7	0.044	14.0	86.0	0.99264	3.56	0.94	12.9	7	1	red
91	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
92	5.2	0.155	0.33	1.6	0.028	13.0	59.0	0.98975	3.3	0.84	11.9	8	1	white
93	5.2	0.16	0.34	0.8	0.029	26.0	77.0	0.99155	3.25	0.51	10.1	6	0	white
94	5.2	0.17	0.27	0.7	0.03	11.0	68.0	0.99218	3.3	0.41	9.8	5	0	white
95	5.2	0.185	0.22	1.0	0.03	47.0	123.0	0.99218	3.55	0.44	10.15	6	0	white
96	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
97	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
98	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
99	5.2	0.24	0.15	7.1	0.043	32.0	134.0	0.99378	3.24	0.48	9.9	6	0	white
100	5.2	0.24	0.45	3.8	0.027	21.0	128.0	0.992	3.55	0.49	11.2	8	1	white

Rows: 1-100 | Columns: 14

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

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 vDataFrame.train_test_split() method. This is a crucial step when preparing your data for machine learning, as it allows you to evaluate the performance of your models accurately.

train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, VerticaPy utilizes seeded randomization to guarantee the reproducibility of your data split. However, please be aware that this approach may lead to reduced performance. For a more efficient data split, you can use the vDataFrame.to_db() method to save your results into tables or temporary tables. This will help enhance the overall performance of the process.

Balancing the Dataset

In VerticaPy, 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, VerticaPy’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 verticapy.machine_learning.vertica.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 verticapy.machine_learning.vertica import KNeighborsClassifier

Then we can create the model:

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

Hint

In verticapy 1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.

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 vDataFrame or the name of the relation stored in the database. The test set is optional and is only used to compute the test metrics. In verticapy, we don’t work using X matrices and y vectors. Instead, we work directly with lists of predictors and the response name.

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()

	value
auc	0.6612807131745315
prc_auc	0.5385520120216585
accuracy	0.657556270096463
log_loss	0.269525468230416
precision	0.6428571428571429
recall	0.19313304721030042
f1_score	0.297029702970297
mcc	0.19736562886403478
informedness	0.1288656950252105
markedness	0.3022774327122155
csi	0.1744186046511628

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	0.6612807131745315
prc_auc	0.5385520120216585
accuracy	0.5514469453376206
log_loss	0.269525468230416
precision	0.4470046082949309
recall	0.8326180257510729
f1_score	0.5817091454272865
mcc	0.2272905313259077
informedness	0.21565144477420928
markedness	0.2395577997842926
csi	0.41014799154334036

Rows: 1-11 | Columns: 2

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")
Out[4]: 0.5095468273527024

Note

For multi-class scoring, verticapy 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 Numeric(8)	123 volatile_acidity Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction Integer
1	4.2	0.17	0.98999	3.65	0.36	1.8	0.029	93.0	161.0	0.89	12.0	7	1	white	7
2	4.7	0.67	0.98722	3.3	0.09	1.0	0.02	5.0	9.0	0.34	13.6	5	0	white	5
3	4.8	0.21	0.99324	3.66	0.21	10.2	0.037	17.0	112.0	0.48	12.2	7	1	white	7
4	5.0	0.24	0.98774	3.32	0.34	1.1	0.034	49.0	158.0	0.32	13.1	7	1	white	6
5	5.0	0.35	0.99241	3.39	0.25	7.8	0.031	24.0	116.0	0.4	11.3	6	0	white	6
6	5.0	0.4	0.9902	3.49	0.5	4.3	0.046	29.0	80.0	0.66	13.6	6	0	red	7
7	5.1	0.26	0.98946	3.35	0.33	1.1	0.027	46.0	113.0	0.43	11.4	7	1	white	7
8	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	6
9	5.1	0.305	0.99	3.4	0.13	1.75	0.036	17.0	73.0	0.51	12.3333333333333	5	0	white	6
10	5.1	0.33	0.9893	3.51	0.22	1.6	0.027	18.0	89.0	0.38	12.5	7	1	white	6
11	5.1	0.35	0.99188	3.38	0.26	6.8	0.034	36.0	120.0	0.4	11.5	6	0	white	6
12	5.1	0.51	0.9924	3.46	0.18	2.1	0.042	16.0	101.0	0.87	12.9	7	1	red	7
13	5.2	0.31	0.98886	3.56	0.2	2.4	0.027	27.0	117.0	0.45	13.0	7	1	white	7
14	5.2	0.48	0.9927	3.54	0.04	1.6	0.054	19.0	106.0	0.62	12.2	7	1	red	7
15	5.3	0.2	0.99278	3.41	0.31	3.6	0.036	22.0	91.0	0.5	9.8	6	0	white	6
16	5.3	0.23	0.99119	3.16	0.56	0.9	0.041	46.0	141.0	0.62	9.7	5	0	white	6
17	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	6
18	5.3	0.31	0.99321	3.34	0.38	10.5	0.031	53.0	140.0	0.46	11.7	6	0	white	6
19	5.4	0.18	0.99445	3.42	0.24	4.8	0.041	30.0	113.0	0.4	9.4	6	0	white	5
20	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	6
21	5.4	0.22	0.99178	3.76	0.29	1.2	0.045	69.0	152.0	0.63	11.0	7	1	white	6
22	5.4	0.31	0.9931	3.29	0.47	3.0	0.053	46.0	144.0	0.76	10.0	5	0	white	7
23	5.5	0.12	0.99164	3.25	0.33	1.0	0.038	23.0	131.0	0.45	9.8	5	0	white	5
24	5.5	0.16	0.9898	3.33	0.31	1.2	0.026	31.0	68.0	0.44	11.65	6	0	white	6
25	5.5	0.16	0.99076	3.43	0.26	1.5	0.032	35.0	100.0	0.77	12.0	6	0	white	6
26	5.5	0.16	0.9938	3.24	0.22	4.5	0.03	30.0	102.0	0.36	9.4	6	0	white	7
27	5.5	0.17	0.99243	3.28	0.23	2.9	0.039	10.0	108.0	0.5	10.0	5	0	white	6
28	5.6	0.13	0.9948	3.34	0.27	4.8	0.028	22.0	104.0	0.45	9.2	6	0	white	5
29	5.6	0.18	0.98984	3.51	0.58	1.25	0.034	29.0	129.0	0.6	12.0	7	1	white	6
30	5.6	0.22	0.98823	3.2	0.32	1.2	0.024	29.0	97.0	0.46	13.05	7	1	white	6
31	5.6	0.26	0.99315	3.44	0.0	10.2	0.038	13.0	111.0	0.46	12.4	6	0	white	7
32	5.6	0.26	0.99428	3.23	0.5	11.4	0.029	25.0	93.0	0.49	10.5	6	0	white	7
33	5.6	0.28	0.99144	3.21	0.4	6.1	0.034	36.0	118.0	0.43	12.1	7	1	white	7
34	5.6	0.295	0.99378	3.21	0.2	2.2	0.049	18.0	134.0	0.68	10.0	5	0	white	5
35	5.6	0.35	0.9902	3.37	0.37	1.0	0.038	6.0	72.0	0.34	11.4	5	0	white	7
36	5.6	0.615	0.9943	3.58	0.0	1.6	0.089	16.0	59.0	0.52	9.9	5	0	red	5
37	5.6	0.66	0.99378	3.71	0.0	2.2	0.087	3.0	11.0	0.63	12.8	7	1	red	7
38	5.7	0.1	0.9928	3.27	0.27	1.3	0.047	21.0	100.0	0.46	9.5	5	0	white	6
39	5.7	0.135	0.9946	3.31	0.3	4.6	0.042	19.0	101.0	0.42	9.3	6	0	white	6
40	5.7	0.21	0.99074	3.24	0.32	0.9	0.038	38.0	121.0	0.46	10.6	6	0	white	6
41	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	6
42	5.7	0.26	0.994	3.39	0.25	10.4	0.02	7.0	57.0	0.37	10.6	5	0	white	7
43	5.7	0.33	0.9934	3.38	0.15	1.9	0.05	20.0	93.0	0.62	9.9	5	0	white	7
44	5.7	0.43	0.992	3.54	0.3	5.7	0.039	24.0	98.0	0.61	12.3	7	1	white	7
45	5.8	0.13	0.99478	3.36	0.26	5.1	0.039	19.0	103.0	0.47	9.3	6	0	white	6
46	5.8	0.19	0.99107	3.16	0.33	4.2	0.038	49.0	133.0	0.42	11.3	7	1	white	6
47	5.8	0.26	0.99044	3.36	0.29	1.0	0.042	35.0	101.0	0.48	11.4	7	1	white	7
48	5.8	0.28	0.992	3.35	0.28	4.2	0.044	52.0	158.0	0.44	10.7	7	1	white	7
49	5.8	0.3	0.98871	3.1	0.42	1.1	0.036	19.0	113.0	0.46	12.6	7	1	white	5
50	5.8	0.3	0.98963	3.23	0.38	4.9	0.039	22.0	86.0	0.58	13.1	7	1	white	6
51	5.8	0.315	0.99704	2.97	0.19	19.4	0.031	28.0	106.0	0.4	10.55	6	0	white	6
52	5.8	0.34	0.9949	3.46	0.16	7.0	0.037	26.0	116.0	0.45	10.0	7	1	white	7
53	5.8	0.36	0.9911	3.34	0.26	3.3	0.038	40.0	153.0	0.55	11.3	6	0	white	7
54	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	5
55	5.8	0.68	0.9944	3.54	0.02	1.8	0.087	21.0	94.0	0.52	10.0	5	0	red	5
56	5.9	0.15	0.9945	3.52	0.31	5.8	0.041	53.0	155.0	0.46	10.5	6	0	white	7
57	5.9	0.17	0.9931	3.68	0.3	1.4	0.042	25.0	119.0	0.72	10.5	6	0	white	5
58	5.9	0.21	0.98984	3.35	0.31	1.8	0.033	45.0	142.0	0.5	12.7	6	0	white	7
59	5.9	0.24	0.98889	3.08	0.28	1.3	0.032	36.0	95.0	0.64	12.9	7	1	white	7
60	5.9	0.24	0.99225	3.39	0.3	2.0	0.033	28.0	92.0	0.69	10.9	7	1	white	6
61	5.9	0.25	0.997	3.24	0.25	11.3	0.052	30.0	165.0	0.44	9.5	6	0	white	5
62	5.9	0.28	0.99542	3.28	0.14	8.6	0.032	30.0	142.0	0.44	9.5	6	0	white	5
63	5.9	0.29	0.99512	3.21	0.16	7.9	0.044	48.0	197.0	0.36	9.4	5	0	white	5
64	5.9	0.3	0.9904	3.19	0.29	1.1	0.036	23.0	56.0	0.38	11.3	5	0	white	6
65	5.9	0.32	0.98945	3.37	0.33	2.1	0.027	35.0	138.0	0.42	12.7	6	0	white	7
66	6.0	0.16	0.9951	3.63	0.3	6.7	0.043	43.0	153.0	0.46	10.6	5	0	white	7
67	6.0	0.17	0.99362	3.32	0.33	6.0	0.036	30.0	111.0	0.58	10.1333333333333	7	1	white	7
68	6.0	0.22	0.98862	3.22	0.28	1.1	0.034	47.0	90.0	0.38	12.6	6	0	white	6
69	6.0	0.23	0.98961	3.36	0.34	1.3	0.025	23.0	111.0	0.37	12.7	6	0	white	7
70	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	5
71	6.0	0.26	0.98944	3.22	0.29	3.1	0.041	37.0	144.0	0.39	12.8	7	1	white	6
72	6.0	0.27	0.99215	3.23	0.32	3.6	0.035	36.0	133.0	0.46	10.8	6	0	white	7
73	6.0	0.27	0.9924	3.28	0.31	5.0	0.043	54.0	170.0	0.52	11.0	6	0	white	7
74	6.0	0.28	0.99126	3.27	0.27	4.1	0.046	50.0	147.0	0.56	11.6	6	0	white	7
75	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	7
76	6.0	0.28	0.99911	3.14	0.29	19.3	0.051	36.0	174.0	0.5	9.0	5	0	white	5
77	6.0	0.29	0.9895	3.31	0.27	2.3	0.044	20.0	117.0	0.41	12.7	7	1	white	7
78	6.0	0.29	0.9937	3.09	0.41	10.8	0.048	55.0	149.0	0.59	10.9666666666667	7	1	white	7
79	6.0	0.29	0.9972	3.33	0.2	12.6	0.045	45.0	187.0	0.42	9.5	5	0	white	7
80	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	7
81	6.0	0.32	0.98912	3.29	0.3	1.9	0.033	41.0	142.0	0.42	12.8	7	1	white	7
82	6.0	0.32	0.99258	3.1	0.33	9.9	0.032	22.0	90.0	0.43	12.1	7	1	white	7
83	6.0	0.34	0.99108	3.39	0.32	3.8	0.044	13.0	116.0	0.44	11.8	7	1	white	7
84	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	6
85	6.0	0.43	0.99222	3.03	0.34	7.6	0.045	25.0	118.0	0.37	11.0	6	0	white	6
86	6.1	0.15	0.99471	3.6	0.29	6.2	0.046	39.0	151.0	0.44	10.6	6	0	white	7
87	6.1	0.16	0.99365	3.32	0.34	6.1	0.034	31.0	114.0	0.58	10.1333333333333	7	1	white	7
88	6.1	0.18	0.98962	3.16	0.38	2.3	0.033	28.0	111.0	0.49	12.4	6	0	white	5
89	6.1	0.2	0.991	3.3	0.17	1.6	0.048	46.0	129.0	0.43	11.4	6	0	white	7
90	6.1	0.25	0.9963	3.14	0.18	10.5	0.049	41.0	124.0	0.35	10.5	5	0	white	6
91	6.1	0.25	0.99782	3.07	0.48	15.8	0.052	25.0	94.0	0.45	9.2	6	0	white	5
92	6.1	0.27	0.99076	3.32	0.31	1.5	0.035	17.0	83.0	0.44	11.1	7	1	white	7
93	6.1	0.27	0.99281	3.22	0.32	6.2	0.048	47.0	161.0	0.6	11.0	6	0	white	6
94	6.1	0.29	0.9893	3.21	0.27	1.7	0.024	13.0	76.0	0.51	12.6	7	1	white	6
95	6.1	0.3	0.9895	3.39	0.3	2.1	0.031	50.0	163.0	0.43	12.7	7	1	white	7
96	6.1	0.37	0.99558	3.22	0.2	7.6	0.031	49.0	170.0	0.48	9.5	5	0	white	5
97	6.1	0.37	0.997	3.17	0.46	12.0	0.042	61.0	210.0	0.59	9.7	6	0	white	5
98	6.1	0.38	0.99309	3.24	0.47	1.4	0.051	59.0	210.0	0.5	9.6	5	0	white	5
99	6.1	0.43	0.9971	3.37	0.35	9.1	0.059	83.0	249.0	0.5	8.5	5	0	white	7
100	6.1	0.44	0.9916	3.26	0.28	4.25	0.032	43.0	132.0	0.47	11.3	7	1	white	5

Rows: 1-100 | Columns: 15

Note

Predictions can be made automatically using the test set, in which case you don’t need to specify the predictors. Alternatively, you can pass only the vDataFrame to the predict() function, but in this case, it’s essential that the column names of the vDataFrame match the predictors and response name in the model.

Probabilities

It is also easy to get the model’s probabilities:

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

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	123 prediction Integer	123 prediction_5 Float(22)	123 prediction_6 Float(22)
1	4.2	0.17	0.98999	3.65	0.36	1.8	0.029	93.0	161.0	0.89	12.0	7	1	white	7	0.3	0.3
2	4.7	0.67	0.98722	3.3	0.09	1.0	0.02	5.0	9.0	0.34	13.6	5	0	white	5	0.4	0.4
3	4.8	0.21	0.99324	3.66	0.21	10.2	0.037	17.0	112.0	0.48	12.2	7	1	white	7	0.3	0.2
4	5.0	0.24	0.98774	3.32	0.34	1.1	0.034	49.0	158.0	0.32	13.1	7	1	white	6	0.1	0.3
5	5.0	0.35	0.99241	3.39	0.25	7.8	0.031	24.0	116.0	0.4	11.3	6	0	white	6	0.2	0.5
6	5.0	0.4	0.9902	3.49	0.5	4.3	0.046	29.0	80.0	0.66	13.6	6	0	red	7	0.1	0.4
7	5.1	0.26	0.98946	3.35	0.33	1.1	0.027	46.0	113.0	0.43	11.4	7	1	white	7	0.2	0.4
8	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	6	0.2	0.6
9	5.1	0.305	0.99	3.4	0.13	1.75	0.036	17.0	73.0	0.51	12.3333333333333	5	0	white	6	0.0	0.3
10	5.1	0.33	0.9893	3.51	0.22	1.6	0.027	18.0	89.0	0.38	12.5	7	1	white	6	0.2	0.4
11	5.1	0.35	0.99188	3.38	0.26	6.8	0.034	36.0	120.0	0.4	11.5	6	0	white	6	0.1	0.7
12	5.1	0.51	0.9924	3.46	0.18	2.1	0.042	16.0	101.0	0.87	12.9	7	1	red	7	0.0	0.4
13	5.2	0.31	0.98886	3.56	0.2	2.4	0.027	27.0	117.0	0.45	13.0	7	1	white	7	0.1	0.3
14	5.2	0.48	0.9927	3.54	0.04	1.6	0.054	19.0	106.0	0.62	12.2	7	1	red	7	0.0	0.2
15	5.3	0.2	0.99278	3.41	0.31	3.6	0.036	22.0	91.0	0.5	9.8	6	0	white	6	0.1	0.7
16	5.3	0.23	0.99119	3.16	0.56	0.9	0.041	46.0	141.0	0.62	9.7	5	0	white	6	0.4	0.3
17	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	6	0.3	0.3
18	5.3	0.31	0.99321	3.34	0.38	10.5	0.031	53.0	140.0	0.46	11.7	6	0	white	6	0.3	0.4
19	5.4	0.18	0.99445	3.42	0.24	4.8	0.041	30.0	113.0	0.4	9.4	6	0	white	5	0.3	0.5
20	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	6	0.3	0.3
21	5.4	0.22	0.99178	3.76	0.29	1.2	0.045	69.0	152.0	0.63	11.0	7	1	white	6	0.0	0.5
22	5.4	0.31	0.9931	3.29	0.47	3.0	0.053	46.0	144.0	0.76	10.0	5	0	white	7	0.1	0.3
23	5.5	0.12	0.99164	3.25	0.33	1.0	0.038	23.0	131.0	0.45	9.8	5	0	white	5	0.4	0.3
24	5.5	0.16	0.9898	3.33	0.31	1.2	0.026	31.0	68.0	0.44	11.65	6	0	white	6	0.4	0.5
25	5.5	0.16	0.99076	3.43	0.26	1.5	0.032	35.0	100.0	0.77	12.0	6	0	white	6	0.3	0.5
26	5.5	0.16	0.9938	3.24	0.22	4.5	0.03	30.0	102.0	0.36	9.4	6	0	white	7	0.4	0.4
27	5.5	0.17	0.99243	3.28	0.23	2.9	0.039	10.0	108.0	0.5	10.0	5	0	white	6	0.3	0.4
28	5.6	0.13	0.9948	3.34	0.27	4.8	0.028	22.0	104.0	0.45	9.2	6	0	white	5	0.2	0.5
29	5.6	0.18	0.98984	3.51	0.58	1.25	0.034	29.0	129.0	0.6	12.0	7	1	white	6	0.1	0.5
30	5.6	0.22	0.98823	3.2	0.32	1.2	0.024	29.0	97.0	0.46	13.05	7	1	white	6	0.2	0.5
31	5.6	0.26	0.99315	3.44	0.0	10.2	0.038	13.0	111.0	0.46	12.4	6	0	white	7	0.2	0.3
32	5.6	0.26	0.99428	3.23	0.5	11.4	0.029	25.0	93.0	0.49	10.5	6	0	white	7	0.3	0.4
33	5.6	0.28	0.99144	3.21	0.4	6.1	0.034	36.0	118.0	0.43	12.1	7	1	white	7	0.3	0.3
34	5.6	0.295	0.99378	3.21	0.2	2.2	0.049	18.0	134.0	0.68	10.0	5	0	white	5	0.5	0.2
35	5.6	0.35	0.9902	3.37	0.37	1.0	0.038	6.0	72.0	0.34	11.4	5	0	white	7	0.4	0.2
36	5.6	0.615	0.9943	3.58	0.0	1.6	0.089	16.0	59.0	0.52	9.9	5	0	red	5	0.5	0.0
37	5.6	0.66	0.99378	3.71	0.0	2.2	0.087	3.0	11.0	0.63	12.8	7	1	red	7	0.3	0.2
38	5.7	0.1	0.9928	3.27	0.27	1.3	0.047	21.0	100.0	0.46	9.5	5	0	white	6	0.2	0.8
39	5.7	0.135	0.9946	3.31	0.3	4.6	0.042	19.0	101.0	0.42	9.3	6	0	white	6	0.2	0.7
40	5.7	0.21	0.99074	3.24	0.32	0.9	0.038	38.0	121.0	0.46	10.6	6	0	white	6	0.1	0.7
41	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	6	0.1	0.8
42	5.7	0.26	0.994	3.39	0.25	10.4	0.02	7.0	57.0	0.37	10.6	5	0	white	7	0.2	0.4
43	5.7	0.33	0.9934	3.38	0.15	1.9	0.05	20.0	93.0	0.62	9.9	5	0	white	7	0.2	0.3
44	5.7	0.43	0.992	3.54	0.3	5.7	0.039	24.0	98.0	0.61	12.3	7	1	white	7	0.3	0.0
45	5.8	0.13	0.99478	3.36	0.26	5.1	0.039	19.0	103.0	0.47	9.3	6	0	white	6	0.1	0.6
46	5.8	0.19	0.99107	3.16	0.33	4.2	0.038	49.0	133.0	0.42	11.3	7	1	white	6	0.2	0.2
47	5.8	0.26	0.99044	3.36	0.29	1.0	0.042	35.0	101.0	0.48	11.4	7	1	white	7	0.3	0.3
48	5.8	0.28	0.992	3.35	0.28	4.2	0.044	52.0	158.0	0.44	10.7	7	1	white	7	0.3	0.2
49	5.8	0.3	0.98871	3.1	0.42	1.1	0.036	19.0	113.0	0.46	12.6	7	1	white	5	0.3	0.3
50	5.8	0.3	0.98963	3.23	0.38	4.9	0.039	22.0	86.0	0.58	13.1	7	1	white	6	0.1	0.5
51	5.8	0.315	0.99704	2.97	0.19	19.4	0.031	28.0	106.0	0.4	10.55	6	0	white	6	0.1	0.5
52	5.8	0.34	0.9949	3.46	0.16	7.0	0.037	26.0	116.0	0.45	10.0	7	1	white	7	0.0	0.1
53	5.8	0.36	0.9911	3.34	0.26	3.3	0.038	40.0	153.0	0.55	11.3	6	0	white	7	0.4	0.3
54	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	5	0.2	0.7
55	5.8	0.68	0.9944	3.54	0.02	1.8	0.087	21.0	94.0	0.52	10.0	5	0	red	5	0.6	0.3
56	5.9	0.15	0.9945	3.52	0.31	5.8	0.041	53.0	155.0	0.46	10.5	6	0	white	7	0.4	0.3
57	5.9	0.17	0.9931	3.68	0.3	1.4	0.042	25.0	119.0	0.72	10.5	6	0	white	5	0.4	0.0
58	5.9	0.21	0.98984	3.35	0.31	1.8	0.033	45.0	142.0	0.5	12.7	6	0	white	7	0.2	0.4
59	5.9	0.24	0.98889	3.08	0.28	1.3	0.032	36.0	95.0	0.64	12.9	7	1	white	7	0.4	0.2
60	5.9	0.24	0.99225	3.39	0.3	2.0	0.033	28.0	92.0	0.69	10.9	7	1	white	6	0.3	0.3
61	5.9	0.25	0.997	3.24	0.25	11.3	0.052	30.0	165.0	0.44	9.5	6	0	white	5	0.4	0.5
62	5.9	0.28	0.99542	3.28	0.14	8.6	0.032	30.0	142.0	0.44	9.5	6	0	white	5	0.4	0.5
63	5.9	0.29	0.99512	3.21	0.16	7.9	0.044	48.0	197.0	0.36	9.4	5	0	white	5	0.4	0.4
64	5.9	0.3	0.9904	3.19	0.29	1.1	0.036	23.0	56.0	0.38	11.3	5	0	white	6	0.4	0.4
65	5.9	0.32	0.98945	3.37	0.33	2.1	0.027	35.0	138.0	0.42	12.7	6	0	white	7	0.1	0.3
66	6.0	0.16	0.9951	3.63	0.3	6.7	0.043	43.0	153.0	0.46	10.6	5	0	white	7	0.3	0.2
67	6.0	0.17	0.99362	3.32	0.33	6.0	0.036	30.0	111.0	0.58	10.1333333333333	7	1	white	7	0.2	0.3
68	6.0	0.22	0.98862	3.22	0.28	1.1	0.034	47.0	90.0	0.38	12.6	6	0	white	6	0.3	0.4
69	6.0	0.23	0.98961	3.36	0.34	1.3	0.025	23.0	111.0	0.37	12.7	6	0	white	7	0.2	0.1
70	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	5	0.4	0.2
71	6.0	0.26	0.98944	3.22	0.29	3.1	0.041	37.0	144.0	0.39	12.8	7	1	white	6	0.3	0.3
72	6.0	0.27	0.99215	3.23	0.32	3.6	0.035	36.0	133.0	0.46	10.8	6	0	white	7	0.2	0.3
73	6.0	0.27	0.9924	3.28	0.31	5.0	0.043	54.0	170.0	0.52	11.0	6	0	white	7	0.1	0.3
74	6.0	0.28	0.99126	3.27	0.27	4.1	0.046	50.0	147.0	0.56	11.6	6	0	white	7	0.0	0.5
75	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	7	0.2	0.3
76	6.0	0.28	0.99911	3.14	0.29	19.3	0.051	36.0	174.0	0.5	9.0	5	0	white	5	0.4	0.2
77	6.0	0.29	0.9895	3.31	0.27	2.3	0.044	20.0	117.0	0.41	12.7	7	1	white	7	0.1	0.2
78	6.0	0.29	0.9937	3.09	0.41	10.8	0.048	55.0	149.0	0.59	10.9666666666667	7	1	white	7	0.5	0.0
79	6.0	0.29	0.9972	3.33	0.2	12.6	0.045	45.0	187.0	0.42	9.5	5	0	white	7	0.1	0.3
80	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	7	0.2	0.3
81	6.0	0.32	0.98912	3.29	0.3	1.9	0.033	41.0	142.0	0.42	12.8	7	1	white	7	0.0	0.3
82	6.0	0.32	0.99258	3.1	0.33	9.9	0.032	22.0	90.0	0.43	12.1	7	1	white	7	0.4	0.2
83	6.0	0.34	0.99108	3.39	0.32	3.8	0.044	13.0	116.0	0.44	11.8	7	1	white	7	0.1	0.4
84	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	6	0.2	0.3
85	6.0	0.43	0.99222	3.03	0.34	7.6	0.045	25.0	118.0	0.37	11.0	6	0	white	6	0.4	0.3
86	6.1	0.15	0.99471	3.6	0.29	6.2	0.046	39.0	151.0	0.44	10.6	6	0	white	7	0.3	0.2
87	6.1	0.16	0.99365	3.32	0.34	6.1	0.034	31.0	114.0	0.58	10.1333333333333	7	1	white	7	0.3	0.2
88	6.1	0.18	0.98962	3.16	0.38	2.3	0.033	28.0	111.0	0.49	12.4	6	0	white	5	0.4	0.3
89	6.1	0.2	0.991	3.3	0.17	1.6	0.048	46.0	129.0	0.43	11.4	6	0	white	7	0.4	0.1
90	6.1	0.25	0.9963	3.14	0.18	10.5	0.049	41.0	124.0	0.35	10.5	5	0	white	6	0.3	0.5
91	6.1	0.25	0.99782	3.07	0.48	15.8	0.052	25.0	94.0	0.45	9.2	6	0	white	5	0.5	0.2
92	6.1	0.27	0.99076	3.32	0.31	1.5	0.035	17.0	83.0	0.44	11.1	7	1	white	7	0.2	0.2
93	6.1	0.27	0.99281	3.22	0.32	6.2	0.048	47.0	161.0	0.6	11.0	6	0	white	6	0.2	0.6
94	6.1	0.29	0.9893	3.21	0.27	1.7	0.024	13.0	76.0	0.51	12.6	7	1	white	6	0.3	0.4
95	6.1	0.3	0.9895	3.39	0.3	2.1	0.031	50.0	163.0	0.43	12.7	7	1	white	7	0.3	0.1
96	6.1	0.37	0.99558	3.22	0.2	7.6	0.031	49.0	170.0	0.48	9.5	5	0	white	5	0.7	0.2
97	6.1	0.37	0.997	3.17	0.46	12.0	0.042	61.0	210.0	0.59	9.7	6	0	white	5	0.6	0.2
98	6.1	0.38	0.99309	3.24	0.47	1.4	0.051	59.0	210.0	0.5	9.6	5	0	white	5	0.4	0.2
99	6.1	0.43	0.9971	3.37	0.35	9.1	0.059	83.0	249.0	0.5	8.5	5	0	white	7	0.5	0.2
100	6.1	0.44	0.9916	3.26	0.28	4.25	0.032	43.0	132.0	0.47	11.3	7	1	white	5	0.6	0.2

Rows: 1-100 | Columns: 17

Note

Probabilities are added to the vDataFrame, and VerticaPy uses the corresponding probability function in SQL behind the scenes. You can use the pos_label parameter to add only the probability of the selected category.

Confusion Matrix

You can obtain the confusion matrix of your choice by specifying the desired cutoff.

model.confusion_matrix(cutoff = 0.5)
Out[5]: 
array([[101,  52],
       [ 63, 109]])

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)
Out[6]: 
array([[388,  22],
       [166,  24]])

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()
Out[7]: {'n_neighbors': 10, 'p': 2}

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

Must be overridden in the child class

Methods

__init__([name, overwrite_model, n_neighbors, p])	Must be overridden in the child class
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.
does_model_exists(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
drop()	KNeighborsClassifier models are not stored in the Vertica DB.
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, or Highcharts) to draw a specific graphic.
get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
import_models(path[, schema, kind])	Imports machine learning models.
lift_chart([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.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
report([metrics, cutoff, 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 Vertica Binary format.
to_pmml(path)	Exports the model to PMML.
to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes

object_type