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	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	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
3	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
4	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
5	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
6	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
7	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
8	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
9	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
10	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
11	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
12	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
13	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
14	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
15	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
16	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
17	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
18	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
19	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
20	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
21	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
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
30	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
31	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
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
45	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
71	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
72	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
73	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
81	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
82	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
83	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	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()

	5	6	7	avg_macro	avg_weighted	avg_micro
auc	0.7218114837398377	0.6722378487848784	0.7179235537190086	0.7039909620812416	0.7036328990494615	[null]
prc_auc	0.6055638110298862	0.4962590789879965	0.6509673760933948	0.5842634220370925	0.583835438950684	[null]
accuracy	0.723338485316847	0.6723338485316847	0.6831530139103554	0.6929417825862957	0.6923310296720806	0.6929417825862957
log_loss	0.247372354179121	0.263088251395399	0.243743081751822	0.25140122910878065	0.25148218022792374	[null]
precision	0.5736842105263158	0.5186915887850467	0.5308641975308642	0.541079998947409	0.5404248911413412	0.5394126738794436
recall	0.5265700483091788	0.5045454545454545	0.5863636363636363	0.5391597130727566	0.5394126738794436	0.5394126738794436
f1_score	0.54911838790932	0.5115207373271888	0.5572354211663066	0.5392915154676051	0.5390940667168443	0.5394126738794436
mcc	0.3507605430140015	0.26513735306409725	0.31242486060023666	0.30944091889277847	0.3086106946059533	0.3091190108191654
informedness	0.3424791392182698	0.2633276559506066	0.3193847136470087	0.308397169605295	0.3077123695976155	0.3091190108191655
markedness	0.35924219739721286	0.26695948716841844	0.3056166727783891	0.31060611911467345	0.3096288872481	0.3091190108191655
csi	0.3784722222222222	0.34365325077399383	0.38622754491017963	0.36945100596879854	0.3692697450548967	0.3693121693121693

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	0.7182268468745199	0.6583389868837625	0.7184113865932049	0.6983257401171624	0.6977992963308579	[null]
prc_auc	0.6003502432077987	0.4991573952497418	0.6211264008896221	0.5735446797823877	0.5728036677514173	[null]
accuracy	0.58603066439523	0.5288461538461539	0.55810147299509	0.5576594304121579	0.5569527713354673	0.5570801317233809
log_loss	0.245790024975802	0.263088251395399	0.249176152265673	0.25268480954562467	0.2528618316457757	[null]
precision	0.4477211796246649	0.4177777777777778	0.4397163120567376	0.43507175648639346	0.4347497730870425	0.43418940609951845
recall	0.8186274509803921	0.8545454545454545	0.8493150684931506	0.8408293246729991	0.8413685847589425	0.8413685847589425
f1_score	0.5788561525129983	0.5611940298507463	0.5794392523364486	0.5731631449000644	0.5730117230816455	0.5727898358920064
mcc	0.27778031238447665	0.2195092751581671	0.2542676925091321	0.2505190933505919	0.24983491278496098	0.25016134804760454
informedness	0.28076844314749394	0.20603060306030585	0.2447232317584569	0.24384075932208557	0.24292050206914173	0.24340420816861164
markedness	0.27482398336298264	0.2338697318007663	0.2641843971631206	0.25762603744228985	0.2571878951492076	0.25710607276618513
csi	0.4073170731707317	0.3900414937759336	0.40789473684210525	0.4017511012629235	0.40160304654114415	0.4013353115727003

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")
Out[4]: 0.3530332983456925

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	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	7
2	5.1	0.11	0.99008	3.57	0.32	1.6	0.028	12.0	90.0	0.52	12.2	6	0	white	7
3	5.1	0.585	0.99264	3.56	0.0	1.7	0.044	14.0	86.0	0.94	12.9	7	1	red	7
4	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
5	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	7
6	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
7	5.6	0.24	0.98981	3.04	0.34	2.0	0.041	14.0	73.0	0.45	11.6	7	1	white	6
8	5.6	0.35	0.9937	3.3	0.14	5.0	0.046	48.0	198.0	0.71	10.3	5	0	white	5
9	5.7	0.24	0.99391	3.11	0.47	6.3	0.069	35.0	182.0	0.46	9.75	5	0	white	7
10	5.7	0.46	0.9932	3.13	0.46	1.4	0.04	31.0	169.0	0.47	8.8	5	0	white	5
11	5.8	0.31	0.98906	3.25	0.32	4.5	0.024	28.0	94.0	0.52	13.7	7	1	white	7
12	5.8	0.31	0.9916	3.18	0.33	1.2	0.036	23.0	99.0	0.6	10.5	6	0	white	5
13	5.8	0.36	0.99212	3.1	0.5	1.0	0.127	63.0	178.0	0.45	9.7	5	0	white	5
14	5.8	0.42	0.989	3.32	0.3	2.2	0.035	26.0	129.0	0.47	12.9	6	0	white	7
15	5.9	0.23	0.99139	3.31	0.24	3.8	0.038	61.0	152.0	0.5	11.3	7	1	white	5
16	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	7
17	6.0	0.13	0.9948	3.59	0.28	5.7	0.038	56.0	189.5	0.43	10.6	7	1	white	6
18	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
19	6.0	0.23	0.99571	3.05	0.15	9.7	0.048	101.0	207.0	0.3	9.1	5	0	white	6
20	6.0	0.24	0.9946	3.52	0.32	6.3	0.03	34.0	129.0	0.41	10.4	5	0	white	7
21	6.0	0.29	0.99658	3.11	0.21	15.55	0.043	20.0	142.0	0.54	10.1	6	0	white	7
22	6.1	0.21	0.99184	3.22	0.19	1.4	0.046	51.0	131.0	0.39	10.5	5	0	white	5
23	6.1	0.22	0.98966	3.03	0.4	1.85	0.031	25.0	111.0	0.3	11.8	7	1	white	6
24	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	5
25	6.1	0.32	0.99633	3.42	0.25	2.3	0.071	23.0	58.0	0.97	10.6	5	0	red	5
26	6.2	0.21	0.9933	3.49	0.27	1.7	0.038	41.0	150.0	0.71	10.5	7	1	white	7
27	6.2	0.32	0.9949	3.18	0.16	7.0	0.045	30.0	136.0	0.47	9.6	6	0	white	5
28	6.2	0.35	0.99005	3.1	0.29	3.9	0.041	22.0	79.0	0.59	12.0666666666667	6	0	white	5
29	6.2	0.36	0.98936	3.2	0.32	4.0	0.036	44.0	92.0	0.5	13.3	7	1	white	7
30	6.2	0.6	0.9949	3.45	0.08	2.0	0.09	32.0	44.0	0.58	10.5	5	0	red	6
31	6.3	0.22	0.99204	3.24	0.3	2.0	0.05	23.0	120.0	0.47	10.4	6	0	white	6
32	6.3	0.27	0.9926	3.45	0.38	0.9	0.051	7.0	140.0	0.5	10.5	7	1	white	6
33	6.3	0.27	0.99691	3.18	0.46	11.1	0.053	44.0	177.0	0.67	9.4	5	0	white	5
34	6.3	0.41	0.99274	3.16	0.16	0.9	0.032	25.0	98.0	0.42	9.5	5	0	white	5
35	6.4	0.15	0.99112	3.17	0.4	1.3	0.053	61.0	146.0	0.68	11.0	6	0	white	7
36	6.4	0.17	0.9916	3.46	0.27	1.5	0.037	20.0	98.0	0.42	11.0	7	1	white	6
37	6.4	0.21	0.99278	3.15	0.28	5.9	0.047	29.0	101.0	0.4	11.0	6	0	white	7
38	6.4	0.24	0.9942	3.01	0.49	5.8	0.053	25.0	120.0	0.98	10.5	6	0	white	5
39	6.4	0.28	0.99354	3.1	0.56	1.7	0.156	49.0	106.0	0.37	9.2	6	0	white	5
40	6.4	0.45	0.9905	2.97	0.07	1.1	0.03	10.0	131.0	0.28	10.8	5	0	white	5
41	6.4	0.57	0.99519	3.47	0.12	2.3	0.12	25.0	36.0	0.71	11.3	7	1	red	6
42	6.5	0.16	0.992	3.18	0.33	4.8	0.043	45.0	114.0	0.44	11.2	6	0	white	7
43	6.5	0.23	0.99	3.19	0.39	1.9	0.036	41.0	98.0	0.43	11.9	7	1	white	6
44	6.5	0.25	0.99776	3.2	0.27	17.4	0.064	29.0	140.0	0.49	10.1	6	0	white	6
45	6.5	0.28	0.99111	3.28	0.34	3.6	0.04	29.0	121.0	0.48	12.1	7	1	white	7
46	6.5	0.28	0.9912	3.32	0.29	2.7	0.038	26.0	107.0	0.41	11.6	7	1	white	7
47	6.5	0.32	0.9943	3.03	0.23	8.5	0.051	20.0	138.0	0.42	10.7	5	0	white	5
48	6.5	0.35	0.99692	3.18	0.31	10.2	0.069	58.0	170.0	0.49	9.4	5	0	white	5
49	6.5	0.36	0.9902	3.1	0.49	2.9	0.03	16.0	94.0	0.49	12.1	7	1	white	5
50	6.6	0.2	0.99496	3.11	0.27	10.9	0.038	29.0	130.0	0.44	10.5	7	1	white	6
51	6.6	0.21	0.99294	2.96	0.5	8.7	0.036	41.0	191.0	0.56	11.0	6	0	white	6
52	6.6	0.22	0.99009	3.15	0.37	1.6	0.04	31.0	101.0	0.66	12.0	5	0	white	7
53	6.6	0.22	0.99019	3.17	0.35	1.4	0.05	23.0	83.0	0.48	12.0	7	1	white	7
54	6.6	0.22	0.99906	3.08	0.23	17.3	0.047	37.0	118.0	0.46	8.8	6	0	white	6
55	6.6	0.23	0.99084	3.29	0.32	1.7	0.024	26.0	102.0	0.6	11.8	6	0	white	6
56	6.6	0.23	0.99756	3.33	0.29	14.45	0.057	29.0	144.0	0.54	10.2	6	0	white	6
57	6.6	0.24	0.99381	3.13	0.3	11.3	0.026	11.0	77.0	0.55	12.8	7	1	white	6
58	6.6	0.29	0.9913	3.15	0.39	6.75	0.031	22.0	98.0	0.8	12.9	7	1	white	5
59	6.6	0.3	0.9956	3.18	0.45	8.0	0.038	54.0	200.0	0.48	9.5	5	0	white	5
60	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	7
61	6.6	0.44	0.99444	3.42	0.09	2.2	0.063	9.0	18.0	0.69	11.3	6	0	red	6
62	6.6	0.64	0.99036	3.11	0.28	4.4	0.032	19.0	78.0	0.62	12.9	6	0	white	5
63	6.7	0.16	0.99666	2.88	0.32	12.5	0.035	18.0	156.0	0.36	9.0	6	0	white	6
64	6.7	0.21	0.9953	3.12	0.42	9.1	0.049	31.0	150.0	0.74	9.9	7	1	white	7
65	6.7	0.28	0.99064	3.26	0.28	2.4	0.012	36.0	100.0	0.39	11.7	7	1	red	7
66	6.7	0.37	0.9953	3.16	0.41	6.3	0.061	22.0	149.0	0.47	9.6	6	0	white	5
67	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	6
68	6.7	0.48	0.9889	3.15	0.32	1.4	0.021	22.0	121.0	0.53	12.7	7	1	white	5
69	6.8	0.16	0.9923	3.49	0.4	2.3	0.037	18.0	102.0	0.42	11.4	7	1	white	6
70	6.8	0.16	0.99518	3.2	0.29	10.4	0.046	59.0	143.0	0.4	10.8	6	0	white	6
71	6.8	0.18	0.99498	3.42	0.32	7.2	0.047	17.0	109.0	0.44	10.4	6	0	white	6
72	6.8	0.21	1.0	3.27	0.36	18.1	0.046	32.0	133.0	0.48	8.8	5	0	white	5
73	6.8	0.22	0.9918	3.24	0.31	6.3	0.035	33.0	170.0	0.66	12.6	6	0	white	6
74	6.8	0.23	0.9988	3.18	0.39	16.1	0.053	71.0	194.0	0.64	10.2	6	0	white	6
75	6.8	0.24	1.00055	3.0	0.49	19.3	0.057	55.0	247.0	0.56	8.7	5	0	white	6
76	6.8	0.25	0.99402	3.53	0.24	1.6	0.045	39.0	164.0	0.58	10.8	5	0	white	6
77	6.8	0.32	0.99061	3.33	0.3	3.3	0.029	15.0	80.0	0.63	12.6	7	1	white	7
78	6.8	0.33	0.99226	3.06	0.31	7.4	0.045	34.0	143.0	0.55	12.2	6	0	white	5
79	6.8	0.36	0.9928	3.36	0.32	1.8	0.067	4.0	8.0	0.55	12.8	7	1	red	7
80	6.8	0.39	0.99212	3.18	0.34	7.4	0.02	38.0	133.0	0.44	12.0	7	1	white	5
81	6.9	0.19	0.99315	3.21	0.35	1.7	0.036	33.0	101.0	0.54	10.8	7	1	white	6
82	6.9	0.25	0.9948	3.16	0.26	5.2	0.024	36.0	135.0	0.72	10.7	7	1	white	6
83	6.9	0.28	0.98882	2.98	0.24	2.1	0.034	49.0	121.0	0.43	13.2	7	1	white	5
84	6.9	0.34	0.99165	3.36	0.3	4.7	0.029	34.0	148.0	0.49	12.3	7	1	white	7
85	7.0	0.2	0.9928	3.19	0.34	5.7	0.035	32.0	83.0	0.46	11.5	6	0	white	6
86	7.0	0.23	0.9912	3.16	0.35	1.4	0.036	31.0	113.0	0.48	10.8	7	1	white	7
87	7.0	0.23	0.99372	3.18	0.28	2.7	0.053	16.0	92.0	0.56	9.3	5	0	white	5
88	7.0	0.24	0.98988	3.16	0.35	1.0	0.032	42.0	104.0	0.37	11.7	7	1	white	6
89	7.0	0.24	0.99	3.06	0.34	1.4	0.031	27.0	107.0	0.39	11.9	6	0	white	7
90	7.0	0.34	0.9937	3.01	0.1	3.5	0.044	17.0	63.0	0.39	9.2	5	0	white	5
91	7.0	0.49	0.9974	3.34	0.49	5.6	0.06	26.0	121.0	0.76	10.5	5	0	red	5
92	7.0	0.55	0.9959	3.36	0.13	2.2	0.075	15.0	35.0	0.59	9.7	6	0	red	5
93	7.1	0.22	0.9899	3.15	0.33	2.8	0.033	48.0	153.0	0.38	12.7	7	1	white	7
94	7.1	0.34	0.99	3.3	0.49	1.5	0.027	26.0	126.0	0.33	12.2	7	1	white	7
95	7.1	0.35	0.9897	3.26	0.27	3.1	0.034	28.0	134.0	0.38	13.1	7	1	white	7
96	7.1	0.365	0.9941	3.15	0.14	1.2	0.055	24.0	84.0	0.43	8.9	5	0	white	5
97	7.1	0.46	0.99624	3.36	0.14	2.8	0.076	15.0	37.0	0.49	10.7	5	0	red	5
98	7.2	0.18	0.9919	3.14	0.41	1.2	0.048	41.0	97.0	0.45	10.4	5	0	white	7
99	7.2	0.23	0.99166	3.18	0.39	1.5	0.053	26.0	106.0	0.47	11.1	6	0	white	6
100	7.2	0.24	0.9958	3.17	0.19	7.7	0.045	53.0	176.0	0.38	9.5	5	0	white	6

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)	123 prediction_7 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	0.4	0.2	0.4
2	4.7	0.145	0.9908	3.76	0.29	1.0	0.042	35.0	90.0	0.49	11.3	6	0	white	7	0.1	0.3	0.6
3	5.0	0.255	0.99238	3.75	0.22	2.7	0.043	46.0	153.0	0.76	11.3	6	0	white	7	0.0	0.3	0.7
4	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	0.1	0.9
5	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	6	0.4	0.3	0.3
6	5.6	0.18	0.9954	3.49	0.3	10.2	0.028	28.0	131.0	0.42	10.8	7	1	white	7	0.1	0.5	0.4
7	5.6	0.49	0.9907	3.42	0.13	4.5	0.039	17.0	116.0	0.9	13.7	7	1	white	7	0.4	0.3	0.3
8	5.6	0.54	0.9942	3.72	0.04	1.7	0.049	5.0	13.0	0.58	11.4	5	0	red	5	0.4	0.2	0.4
9	5.8	0.19	0.9935	3.34	0.49	4.9	0.04	44.0	118.0	0.38	9.5	7	1	white	7	0.2	0.2	0.6
10	5.8	0.19	0.99362	3.77	0.24	1.3	0.044	38.0	128.0	0.6	10.6	5	0	white	5	0.5	0.0	0.5
11	5.8	0.31	0.98906	3.25	0.32	4.5	0.024	28.0	94.0	0.52	13.7	7	1	white	7	0.0	0.4	0.6
12	5.8	0.35	0.9912	3.35	0.29	3.2	0.034	41.0	151.0	0.58	11.6333333333333	7	1	white	7	0.1	0.2	0.7
13	5.8	0.36	0.99212	3.1	0.5	1.0	0.127	63.0	178.0	0.45	9.7	5	0	white	5	0.4	0.2	0.4
14	5.9	0.19	0.9897	3.09	0.37	0.8	0.027	3.0	21.0	0.31	10.8	5	0	white	7	0.2	0.2	0.6
15	5.9	0.19	0.99341	3.32	0.21	1.7	0.045	57.0	135.0	0.44	9.5	5	0	white	7	0.4	0.1	0.5
16	5.9	0.2	0.99426	3.31	0.28	12.8	0.038	29.0	132.0	0.57	11.8	7	1	white	5	0.5	0.1	0.4
17	5.9	0.32	0.98964	3.22	0.28	4.7	0.039	34.0	94.0	0.57	13.1	7	1	white	5	0.6	0.2	0.2
18	5.9	0.34	0.98892	3.4	0.31	2.0	0.03	38.0	142.0	0.41	12.9	7	1	white	7	0.1	0.3	0.6
19	5.9	0.42	0.99184	3.25	0.36	2.4	0.034	19.0	77.0	0.48	10.9	5	0	white	5	0.6	0.2	0.2
20	6.0	0.13	0.9948	3.59	0.28	5.7	0.038	56.0	189.5	0.43	10.6	7	1	white	6	0.1	0.4	0.5
21	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	0.2	0.5	0.3
22	6.0	0.25	0.99398	3.16	0.4	5.7	0.052	56.0	152.0	0.88	10.5	6	0	white	7	0.3	0.3	0.4
23	6.0	0.34	0.9951	3.25	0.24	5.4	0.06	23.0	126.0	0.44	9.0	7	1	white	7	0.2	0.4	0.4
24	6.1	0.32	0.99633	3.42	0.25	2.3	0.071	23.0	58.0	0.97	10.6	5	0	red	7	0.2	0.2	0.6
25	6.1	0.35	0.9934	3.23	0.07	1.4	0.069	22.0	108.0	0.52	9.2	5	0	white	5	0.6	0.2	0.2
26	6.2	0.15	0.993	3.38	0.46	1.6	0.039	38.0	123.0	0.51	9.7	6	0	white	7	0.1	0.4	0.5
27	6.2	0.7	0.99622	3.54	0.15	5.1	0.076	13.0	27.0	0.6	11.9	6	0	red	6	0.6	0.4	0.0
28	6.3	0.22	0.98998	3.14	0.28	2.4	0.042	38.0	102.0	0.37	11.6	7	1	white	6	0.3	0.6	0.1
29	6.3	0.25	0.9968	3.18	0.44	11.6	0.041	48.0	195.0	0.52	9.5	5	0	white	6	0.4	0.3	0.3
30	6.3	0.27	0.9936	3.28	0.23	2.9	0.047	13.0	100.0	0.43	9.8	5	0	white	5	0.3	0.6	0.1
31	6.3	0.3	0.9959	3.44	0.48	1.8	0.069	18.0	61.0	0.78	10.3	6	0	red	7	0.0	0.4	0.6
32	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	6	0.1	0.5	0.4
33	6.5	0.08	0.991	3.34	0.33	1.9	0.028	23.0	93.0	0.7	12.0	7	1	white	7	0.1	0.1	0.8
34	6.5	0.24	0.98928	3.25	0.28	1.1	0.034	26.0	83.0	0.33	12.3	6	0	white	7	0.2	0.3	0.5
35	6.5	0.25	0.99776	3.2	0.27	17.4	0.064	29.0	140.0	0.49	10.1	6	0	white	6	0.3	0.4	0.3
36	6.5	0.25	0.998	3.58	0.35	12.0	0.055	47.0	179.0	0.47	10.0	5	0	white	7	0.3	0.3	0.4
37	6.5	0.28	0.99074	3.17	0.25	4.8	0.029	54.0	128.0	0.44	12.2	7	1	white	6	0.3	0.6	0.1
38	6.5	0.35	0.99692	3.18	0.31	10.2	0.069	58.0	170.0	0.49	9.4	5	0	white	5	0.4	0.3	0.3
39	6.6	0.2	0.99496	3.11	0.27	10.9	0.038	29.0	130.0	0.44	10.5	7	1	white	6	0.0	0.5	0.5
40	6.6	0.22	0.99906	3.08	0.23	17.3	0.047	37.0	118.0	0.46	8.8	6	0	white	6	0.2	0.6	0.2
41	6.6	0.23	0.99084	3.29	0.32	1.7	0.024	26.0	102.0	0.6	11.8	6	0	white	6	0.3	0.5	0.2
42	6.6	0.24	0.99381	3.13	0.3	11.3	0.026	11.0	77.0	0.55	12.8	7	1	white	6	0.3	0.4	0.3
43	6.6	0.26	0.9981	3.11	0.56	15.4	0.053	32.0	141.0	0.49	9.3	5	0	white	5	0.4	0.4	0.2
44	6.6	0.56	0.99397	3.42	0.14	2.4	0.064	13.0	29.0	0.62	11.7	7	1	red	6	0.4	0.4	0.2
45	6.6	0.64	0.99036	3.11	0.28	4.4	0.032	19.0	78.0	0.62	12.9	6	0	white	5	0.6	0.4	0.0
46	6.7	0.19	0.98912	3.31	0.39	1.0	0.032	14.0	71.0	0.38	13.0	7	1	white	6	0.0	0.6	0.4
47	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	6	0.0	0.6	0.4
48	6.7	0.21	0.98949	3.24	0.34	1.5	0.035	45.0	123.0	0.36	12.6	7	1	white	7	0.0	0.5	0.5
49	6.7	0.24	0.99802	3.42	0.26	12.6	0.053	44.0	182.0	0.42	9.7	5	0	white	7	0.3	0.3	0.4
50	6.7	0.3	0.9974	3.04	0.5	12.1	0.045	38.0	127.0	0.53	8.9	6	0	white	6	0.2	0.4	0.4
51	6.7	0.31	0.98867	3.09	0.3	2.4	0.038	30.0	83.0	0.36	12.8	7	1	white	6	0.3	0.5	0.2
52	6.7	0.34	0.9982	3.19	0.3	15.6	0.054	51.0	196.0	0.49	9.3	5	0	white	5	0.6	0.2	0.2
53	6.7	0.35	0.99188	3.13	0.32	9.0	0.032	29.0	113.0	0.65	12.9	7	1	white	7	0.2	0.4	0.4
54	6.8	0.17	0.993	3.0	0.17	5.1	0.049	26.0	82.0	0.38	9.8	6	0	white	7	0.0	0.3	0.7
55	6.8	0.21	1.0	3.27	0.36	18.1	0.046	32.0	133.0	0.48	8.8	5	0	white	6	0.4	0.4	0.2
56	6.8	0.22	0.9918	3.24	0.31	6.3	0.035	33.0	170.0	0.66	12.6	6	0	white	6	0.4	0.6	0.0
57	6.8	0.22	0.9984	3.44	0.3	13.6	0.055	50.0	180.0	0.39	9.8	5	0	white	6	0.1	0.6	0.3
58	6.8	0.23	0.9988	3.18	0.39	16.1	0.053	71.0	194.0	0.64	10.2	6	0	white	6	0.4	0.5	0.1
59	6.8	0.3	0.9912	3.09	0.35	2.8	0.038	10.0	164.0	0.53	12.0	6	0	white	6	0.5	0.4	0.1
60	6.8	0.36	0.9928	3.36	0.32	1.8	0.067	4.0	8.0	0.55	12.8	7	1	red	7	0.2	0.1	0.7
61	6.8	0.4	0.9904	3.12	0.29	2.8	0.044	27.0	97.0	0.42	11.2	6	0	white	7	0.4	0.2	0.4
62	6.8	0.59	0.9962	3.41	0.06	6.0	0.06	11.0	18.0	0.59	10.8	7	1	red	6	0.3	0.5	0.2
63	6.8	0.69	0.9997	3.46	0.0	5.6	0.124	21.0	58.0	0.72	10.2	5	0	red	6	0.2	0.6	0.2
64	6.9	0.18	0.9934	3.27	0.36	1.3	0.036	40.0	117.0	0.95	9.5	7	1	white	7	0.1	0.3	0.6
65	6.9	0.2	0.992	3.38	0.37	6.2	0.027	24.0	97.0	0.49	12.2	7	1	white	5	0.4	0.4	0.2
66	6.9	0.25	0.9948	3.16	0.26	5.2	0.024	36.0	135.0	0.72	10.7	7	1	white	6	0.3	0.6	0.1
67	6.9	0.33	0.99226	3.11	0.31	7.7	0.04	29.0	135.0	0.57	12.3	5	0	white	5	0.6	0.3	0.1
68	6.9	0.38	0.9932	3.38	0.32	8.5	0.044	36.0	152.0	0.35	12.0	7	1	white	7	0.2	0.3	0.5
69	6.9	0.43	0.99594	3.17	0.28	9.4	0.056	29.0	183.0	0.43	9.4	5	0	white	5	0.5	0.2	0.3
70	6.9	0.52	0.99685	3.46	0.25	2.6	0.081	10.0	37.0	0.5	11.0	5	0	red	5	0.4	0.3	0.3
71	7.0	0.14	0.9956	3.22	0.32	9.0	0.039	54.0	141.0	0.43	9.4	6	0	white	6	0.2	0.5	0.3
72	7.0	0.16	0.9936	3.08	0.73	1.0	0.138	58.0	150.0	0.3	9.2	5	0	white	7	0.1	0.5	0.4
73	7.0	0.16	0.998	2.91	0.25	14.3	0.044	27.0	149.0	0.46	9.2	6	0	white	7	0.1	0.3	0.6
74	7.0	0.24	0.99436	3.2	0.3	6.7	0.039	37.0	125.0	0.39	9.9	5	0	white	7	0.4	0.4	0.2
75	7.0	0.36	0.99558	3.4	0.21	2.3	0.086	20.0	65.0	0.54	10.1	6	0	red	7	0.3	0.2	0.5
76	7.0	0.49	0.9974	3.34	0.49	5.6	0.06	26.0	121.0	0.76	10.5	5	0	red	5	0.5	0.5	0.0
77	7.1	0.17	0.9958	3.35	0.38	7.4	0.052	49.0	182.0	0.52	9.6	6	0	white	6	0.2	0.4	0.4
78	7.1	0.26	0.9986	3.07	0.34	14.4	0.067	35.0	189.0	0.53	9.1	7	1	white	7	0.2	0.1	0.7
79	7.1	0.28	0.99069	3.15	0.31	1.5	0.053	20.0	98.0	0.5	11.4	5	0	white	5	0.6	0.2	0.2
80	7.1	0.35	0.9897	3.26	0.27	3.1	0.034	28.0	134.0	0.38	13.1	7	1	white	7	0.2	0.2	0.6
81	7.1	0.44	0.9896	3.07	0.37	2.7	0.041	35.0	128.0	0.43	13.5	7	1	white	7	0.4	0.1	0.5
82	7.1	0.75	0.99242	3.39	0.01	2.2	0.059	11.0	18.0	0.4	12.8	6	0	red	5	0.6	0.3	0.1
83	7.2	0.16	0.98958	3.12	0.29	1.0	0.031	40.0	123.0	0.4	12.1	7	1	white	5	0.3	0.2	0.5
84	7.2	0.16	0.9922	3.27	0.32	0.8	0.04	50.0	121.0	0.33	10.0	6	0	white	5	0.3	0.3	0.4
85	7.2	0.18	0.9925	3.32	0.31	1.1	0.045	20.0	73.0	0.4	10.8	7	1	white	5	0.3	0.5	0.2
86	7.2	0.22	0.99196	3.25	0.24	1.4	0.041	17.0	159.0	0.53	11.2	6	0	white	5	0.6	0.1	0.3
87	7.2	0.23	0.9979	2.98	0.39	14.2	0.058	49.0	192.0	0.48	9.0	7	1	white	6	0.0	0.4	0.6
88	7.2	0.24	0.99411	3.36	0.29	3.0	0.036	17.0	117.0	0.68	10.1	6	0	white	6	0.2	0.5	0.3
89	7.2	0.24	0.9958	3.17	0.19	7.7	0.045	53.0	176.0	0.38	9.5	5	0	white	6	0.3	0.5	0.2
90	7.2	0.25	0.9999	2.97	0.39	18.95	0.038	42.0	155.0	0.47	9.0	6	0	white	7	0.1	0.3	0.6
91	7.2	0.29	0.9977	3.08	0.4	13.6	0.045	66.0	231.0	0.59	9.6	6	0	white	7	0.3	0.4	0.3
92	7.2	0.32	0.99568	3.2	0.4	8.7	0.038	45.0	154.0	0.47	10.4	6	0	white	5	0.6	0.2	0.2
93	7.2	0.37	0.9903	3.3	0.15	2.0	0.029	27.0	87.0	0.59	12.6	7	1	white	7	0.1	0.1	0.8
94	7.2	0.61	0.99641	3.25	0.08	4.0	0.082	26.0	108.0	0.51	9.4	5	0	red	6	0.5	0.4	0.1
95	7.3	0.25	0.9911	3.29	0.41	1.8	0.037	52.0	165.0	0.39	12.2	7	1	white	7	0.2	0.3	0.5
96	7.3	0.25	0.9986	3.04	0.36	13.1	0.05	35.0	200.0	0.46	8.9	7	1	white	7	0.0	0.4	0.6
97	7.3	0.26	1.0	3.06	0.33	17.85	0.049	41.5	195.0	0.44	9.1	7	1	white	7	0.0	0.5	0.5
98	7.3	0.4	0.9969	3.41	0.3	1.7	0.08	33.0	79.0	0.65	9.5	6	0	red	5	0.7	0.1	0.2
99	7.3	0.45	0.9978	3.33	0.36	5.9	0.074	12.0	87.0	0.83	10.5	5	0	red	5	0.5	0.2	0.3
100	7.4	0.13	0.995	3.36	0.39	4.7	0.042	36.0	137.0	0.56	10.3	7	1	white	7	0.1	0.3	0.6

Rows: 1-100 | Columns: 18

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([[108,  49,  50],
       [ 47, 113,  60],
       [ 34,  52, 134]])

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([[375,   9],
       [178,  27]])

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