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verticapy.machine_learning.vertica.ensemble.XGBClassifier

class verticapy.machine_learning.vertica.ensemble.XGBClassifier(name: str = None, overwrite_model: bool = False, max_ntree: int = 10, max_depth: int = 5, nbins: int = 32, split_proposal_method: Literal['local', 'global'] = 'global', tol: float = 0.001, learning_rate: float = 0.1, min_split_loss: float = 0.0, weight_reg: float = 0.0, sample: float = 1.0, col_sample_by_tree: float = 1.0, col_sample_by_node: float = 1.0)

Creates an XGBClassifier object using the Vertica XGB_CLASSIFIER algorithm.

Parameters

name: str, optional

Name of the model. The model is stored in the DB.

overwrite_model: bool, optional

If set to True, training a model with the same name as an existing model overwrites the existing model.

max_ntree: int, optional

Maximum number of trees that can be created.

max_depth: int, optional

aximum depth of each tree, an integer between 1 and 20, inclusive.

nbins: int, optional

Number of bins used to find splits in each column, where more splits leads to a longer runtime but more fine-grained, possibly better splits. Must be an integer between 2 and 1000, inclusive.

split_proposal_method: str, optional

Approximate splitting strategy, either global or local (not yet supported).

tol: float, optional

Approximation error of quantile summary structures used in the approximate split finding method.

learning_rate: float, optional

Weight applied to each tree’s prediction. This reduces each tree’s impact, allowing for later trees to contribute and keeping earlier trees from dominating.

min_split_loss: float, optional

Each split must improve the model’s objective function value by at least this much in order to avoid pruning. A value of 0 is the same as turning off this parameter (trees are still pruned based on positive / negative objective function values).

weight_reg: float, optional

Regularization term that is applied to the weights of the leaves in the regression tree. A higher value leads to more sparse/smooth weights, which often helps to prevent overfitting.

sample: float, optional

Fraction of rows used per iteration in training.

col_sample_by_tree: float, optional

float in the range (0,1] that specifies the fraction of columns (features), chosen at random, to use when building each tree.

col_sample_by_node: float, optional

float in the range (0,1] that specifies the fraction of columns (features), chosen at random, to use when evaluating each split.

Attributes

Many attributes are created during the fitting phase.

trees_: list of BinaryTreeClassifier

Tree models are instances of ` BinaryTreeClassifier, each possessing various attributes. For more detailed information, refer to the documentation for BinaryTreeClassifier.

features_importance_: numpy.array

The importance of features. It is calculated using the average gain of each tree. To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

features_importance_trees_: dict of numpy.array

Each element of the array represents the feature importance of tree i. The importance of features is calculated using the average gain of each tree. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

logodds_: numpy.array

The log-odds. It quantifies the logarithm of the odds ratio, providing a measure of the likelihood of an event occurring.

eta_: float

The learning rate, is a crucial hyperparameter in machine learning algorithms. It determines the step size at each iteration during the model training process. A well-chosen learning rate is essential for achieving optimal convergence and preventing overshooting or slow convergence in the training phase. Adjusting the learning rate is often necessary to strike a balance between model accuracy and computational efficiency.

n_estimators_: int

The number of model estimators.

classes_: numpy.array

The classes labels.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_vertica_attributes() method.

Examples

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Important

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

Load data for machine learning

We import 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.

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.

data = vpd.load_winequality()
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 XGBClassifier model:

from verticapy.machine_learning.vertica import XGBClassifier

Then we can create the model:

model = XGBClassifier(
    max_ntree = 3,
    max_depth = 3,
    nbins = 6,
    split_proposal_method = 'global',
    tol = 0.001,
    learning_rate = 0.1,
    min_split_loss = 0,
    weight_reg = 0,
    sample = 0.7,
    col_sample_by_tree = 1,
    col_sample_by_node = 1,
)

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.

Important

The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Training

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "good",
    test,
)



===========
call_string
===========
xgb_classifier('"public"."_verticapy_tmp_xgbclassifier_v_demo_deb55a7255a311ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_dec233fa55a311ef880f0242ac120002_"', '"good"', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', max_ntree=3, max_depth=3, learning_rate=0.1, min_split_loss=0, weight_reg=0, nbins=6, objective=crossentropy, sampling_size=0.7, col_sample_by_tree=1, col_sample_by_node=1)

=======
details
=======
   predictor    |      type      
----------------+----------------
 fixed_acidity  |float or numeric
volatile_acidity|float or numeric
  citric_acid   |float or numeric
 residual_sugar |float or numeric
   chlorides    |float or numeric
    density     |float or numeric


==================
initial_prediction
==================
response_label| value  
--------------+--------
      0       | 0.00000
      1       | 0.00000


===============
Additional Info
===============
       Name       |Value
------------------+-----
    tree_count    |  3  
rejected_row_count|  0  
accepted_row_count|5191 

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.

Features Importance

We can conveniently get the features importance:

result = model.features_importance()

Note

In models such as XGBoost, feature importance is calculated using the average gain of each tree. To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them.

Metrics

We can get the entire report using:

model.report()

	value
auc	0.6603672643146329
prc_auc	0.5093002878329209
accuracy	0.7963246554364471
log_loss	0.261086415021515
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.20367534456355285
csi	0.0

Rows: 1-11 | Columns: 2

Important

Most metrics are computed using a single SQL query, but some of them might require multiple SQL queries. Selecting only the necessary metrics in the report can help optimize performance. E.g. model.report(metrics = ["auc", "accuracy"]).

For classification models, we can easily modify the cutoff to observe the effect on different metrics:

model.report(cutoff = 0.2)

	value
auc	0.6603672643146329
prc_auc	0.5093002878329209
accuracy	0.20367534456355282
log_loss	0.261086415021515
precision	0.20367534456355282
recall	1.0
f1_score	0.33842239185750633
mcc	0.0
informedness	0.0
markedness	-0.7963246554364471
csi	0.20367534456355282

Rows: 1-11 | Columns: 2

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

model.score()
Out[3]: 0.7963246554364471

Prediction

Prediction is straight-forward:

model.predict(
    test,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "prediction",
)

	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)	Abc prediction Varchar(1)
1	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	0
2	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	0
3	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	0
4	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	0
5	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	0
6	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	0
7	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	0
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	0
9	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	0
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	0
11	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	0
12	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	0
13	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	0
14	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	0
15	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	0
16	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	0
17	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7	1	white	0
18	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	0
19	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	0
20	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	0
21	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	0
22	5.3	0.26	0.23	5.15	0.034	48.0	160.0	0.9952	3.82	0.51	10.5	7	1	white	0
23	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white	0
24	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	0
25	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	0
26	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0
27	5.4	0.31	0.47	3.0	0.053	46.0	144.0	0.9931	3.29	0.76	10.0	5	0	white	0
28	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	0
29	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0
30	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0
31	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	0
32	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	0
33	5.5	0.3	0.25	1.9	0.029	33.0	118.0	0.98972	3.36	0.66	12.5	6	0	white	0
34	5.5	0.335	0.3	2.5	0.071	27.0	128.0	0.9924	3.14	0.51	9.6	6	0	white	0
35	5.5	0.62	0.33	1.7	0.037	24.0	118.0	0.98758	3.15	0.39	13.55	6	0	white	0
36	5.6	0.13	0.27	4.8	0.028	22.0	104.0	0.9948	3.34	0.45	9.2	6	0	white	0
37	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white	0
38	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	0
39	5.6	0.18	0.31	1.5	0.038	16.0	84.0	0.9924	3.34	0.58	10.1	6	0	white	0
40	5.6	0.18	0.58	1.25	0.034	29.0	129.0	0.98984	3.51	0.6	12.0	7	1	white	0
41	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	0
42	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	0
43	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	0
44	5.6	0.21	0.24	4.4	0.027	37.0	150.0	0.991	3.3	0.31	11.5	7	1	white	0
45	5.6	0.23	0.25	8.0	0.043	31.0	101.0	0.99429	3.19	0.42	10.4	6	0	white	0
46	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	0
47	5.6	0.245	0.25	9.7	0.032	12.0	68.0	0.994	3.31	0.34	10.5	5	0	white	0
48	5.6	0.245	0.32	1.1	0.047	24.0	152.0	0.9927	3.12	0.42	9.3	6	0	white	0
49	5.6	0.26	0.0	10.2	0.038	13.0	111.0	0.99315	3.44	0.46	12.4	6	0	white	0
50	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	0
51	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	0
52	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	0
53	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0
54	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white	0
55	5.6	0.41	0.22	7.1	0.05	44.0	154.0	0.9931	3.3	0.4	10.5	5	0	white	0
56	5.6	0.46	0.24	4.8	0.042	24.0	72.0	0.9908	3.29	0.37	12.6	6	0	white	0
57	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	0
58	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	0
59	5.7	0.21	0.25	1.1	0.035	26.0	81.0	0.9902	3.31	0.52	11.4	6	0	white	0
60	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	0
61	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	0
62	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	0
63	5.7	0.24	0.3	1.3	0.03	25.0	98.0	0.98968	3.37	0.43	12.4	7	1	white	0
64	5.7	0.24	0.47	6.3	0.069	35.0	182.0	0.99391	3.11	0.46	9.75	5	0	white	0
65	5.7	0.25	0.27	10.8	0.05	58.0	116.0	0.99592	3.1	0.5	9.8	6	0	white	0
66	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0
67	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0
68	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	0
69	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	0
70	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	0
71	5.7	0.28	0.28	2.2	0.019	15.0	65.0	0.9902	3.06	0.52	11.2	6	0	white	0
72	5.7	0.335	0.34	1.0	0.04	13.0	174.0	0.992	3.27	0.66	10.0	5	0	white	0
73	5.7	0.41	0.21	1.9	0.048	30.0	112.0	0.99138	3.29	0.55	11.2	6	0	white	0
74	5.7	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	0
75	5.7	1.13	0.09	1.5	0.172	7.0	19.0	0.994	3.5	0.48	9.8	4	0	red	0
76	5.8	0.13	0.22	12.7	0.058	24.0	183.0	0.9956	3.32	0.42	11.7	6	0	white	0
77	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	0
78	5.8	0.17	0.36	1.3	0.036	11.0	70.0	0.99202	3.43	0.68	10.4	7	1	white	0
79	5.8	0.2	0.34	1.0	0.035	40.0	86.0	0.98993	3.5	0.42	11.7	5	0	white	0
80	5.8	0.21	0.32	1.6	0.045	38.0	95.0	0.98946	3.23	0.94	12.4	8	1	white	0
81	5.8	0.23	0.2	2.0	0.043	39.0	154.0	0.99226	3.21	0.39	10.2	6	0	white	0
82	5.8	0.24	0.28	1.4	0.038	40.0	76.0	0.98711	3.1	0.29	13.9	7	1	white	0
83	5.8	0.24	0.39	1.5	0.054	37.0	158.0	0.9932	3.21	0.52	9.3	6	0	white	0
84	5.8	0.24	0.44	3.5	0.029	5.0	109.0	0.9913	3.53	0.43	11.7	3	0	white	0
85	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0
86	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0
87	5.8	0.27	0.2	7.3	0.04	42.0	145.0	0.99442	3.15	0.48	9.8	5	0	white	0
88	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	0
89	5.8	0.28	0.34	4.0	0.031	40.0	99.0	0.9896	3.39	0.39	12.8	7	1	white	0
90	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	0
91	5.8	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	0
92	5.8	0.29	0.15	1.1	0.029	12.0	83.0	0.9898	3.3	0.4	11.4	6	0	white	0
93	5.8	0.29	0.38	10.7	0.038	49.0	136.0	0.99366	3.11	0.59	11.2	6	0	white	0
94	5.8	0.31	0.31	7.5	0.052	55.0	230.0	0.9949	3.19	0.46	9.8	5	0	white	0
95	5.8	0.32	0.28	4.3	0.032	46.0	115.0	0.98946	3.16	0.57	13.0	8	1	white	0
96	5.8	0.34	0.21	6.6	0.04	50.0	167.0	0.9941	3.29	0.62	10.0	5	0	white	0
97	5.8	0.36	0.32	1.7	0.033	22.0	96.0	0.9898	3.03	0.38	11.2	6	0	white	0
98	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	0
99	5.8	0.4	0.42	4.4	0.047	38.5	245.0	0.9937	3.25	0.57	9.6	6	0	white	0
100	5.8	0.58	0.0	1.5	0.02	33.0	96.0	0.98918	3.29	0.38	12.4	6	0	white	0

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",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density",
    ],
    "prediction",
)

	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)	Abc prediction Varchar(1)	Abc prediction_0 Varchar(128)	Abc prediction_1 Varchar(128)
1	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	0	0.55312	0.44688
2	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	0	0.585598	0.414402
3	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	0	0.55312	0.44688
4	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	0	0.55312	0.44688
5	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	0	0.585598	0.414402
6	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	0	0.585598	0.414402
7	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	0	0.55312	0.44688
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	0	0.55312	0.44688
9	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	0	0.585598	0.414402
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	0	0.55312	0.44688
11	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	0	0.585598	0.414402
12	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	0	0.585598	0.414402
13	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	0	0.585598	0.414402
14	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	0	0.585598	0.414402
15	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	0	0.585598	0.414402
16	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	0	0.55312	0.44688
17	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7	1	white	0	0.55312	0.44688
18	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	0	0.585598	0.414402
19	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	0	0.55312	0.44688
20	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	0	0.55312	0.44688
21	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	0	0.55312	0.44688
22	5.3	0.26	0.23	5.15	0.034	48.0	160.0	0.9952	3.82	0.51	10.5	7	1	white	0	0.585598	0.414402
23	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white	0	0.585598	0.414402
24	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	0	0.585598	0.414402
25	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	0	0.55312	0.44688
26	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0	0.55312	0.44688
27	5.4	0.31	0.47	3.0	0.053	46.0	144.0	0.9931	3.29	0.76	10.0	5	0	white	0	0.55312	0.44688
28	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	0	0.585598	0.414402
29	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0	0.585598	0.414402
30	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0	0.585598	0.414402
31	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	0	0.55312	0.44688
32	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	0	0.55312	0.44688
33	5.5	0.3	0.25	1.9	0.029	33.0	118.0	0.98972	3.36	0.66	12.5	6	0	white	0	0.585598	0.414402
34	5.5	0.335	0.3	2.5	0.071	27.0	128.0	0.9924	3.14	0.51	9.6	6	0	white	0	0.55312	0.44688
35	5.5	0.62	0.33	1.7	0.037	24.0	118.0	0.98758	3.15	0.39	13.55	6	0	white	0	0.55312	0.44688
36	5.6	0.13	0.27	4.8	0.028	22.0	104.0	0.9948	3.34	0.45	9.2	6	0	white	0	0.585598	0.414402
37	5.6	0.18	0.29	2.3	0.04	5.0	47.0	0.99126	3.07	0.45	10.1	4	0	white	0	0.55312	0.44688
38	5.6	0.18	0.3	10.2	0.028	28.0	131.0	0.9954	3.49	0.42	10.8	7	1	white	0	0.55312	0.44688
39	5.6	0.18	0.31	1.5	0.038	16.0	84.0	0.9924	3.34	0.58	10.1	6	0	white	0	0.55312	0.44688
40	5.6	0.18	0.58	1.25	0.034	29.0	129.0	0.98984	3.51	0.6	12.0	7	1	white	0	0.55312	0.44688
41	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	0	0.585598	0.414402
42	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	0	0.585598	0.414402
43	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	0	0.55312	0.44688
44	5.6	0.21	0.24	4.4	0.027	37.0	150.0	0.991	3.3	0.31	11.5	7	1	white	0	0.585598	0.414402
45	5.6	0.23	0.25	8.0	0.043	31.0	101.0	0.99429	3.19	0.42	10.4	6	0	white	0	0.585598	0.414402
46	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	0	0.55312	0.44688
47	5.6	0.245	0.25	9.7	0.032	12.0	68.0	0.994	3.31	0.34	10.5	5	0	white	0	0.585598	0.414402
48	5.6	0.245	0.32	1.1	0.047	24.0	152.0	0.9927	3.12	0.42	9.3	6	0	white	0	0.55312	0.44688
49	5.6	0.26	0.0	10.2	0.038	13.0	111.0	0.99315	3.44	0.46	12.4	6	0	white	0	0.585598	0.414402
50	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	0	0.585598	0.414402
51	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	0	0.55312	0.44688
52	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	0	0.585598	0.414402
53	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0	0.55312	0.44688
54	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white	0	0.585598	0.414402
55	5.6	0.41	0.22	7.1	0.05	44.0	154.0	0.9931	3.3	0.4	10.5	5	0	white	0	0.585598	0.414402
56	5.6	0.46	0.24	4.8	0.042	24.0	72.0	0.9908	3.29	0.37	12.6	6	0	white	0	0.585598	0.414402
57	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	0	0.585598	0.414402
58	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	0	0.585598	0.414402
59	5.7	0.21	0.25	1.1	0.035	26.0	81.0	0.9902	3.31	0.52	11.4	6	0	white	0	0.585598	0.414402
60	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	0	0.605757	0.394243
61	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	0	0.55312	0.44688
62	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	0	0.55312	0.44688
63	5.7	0.24	0.3	1.3	0.03	25.0	98.0	0.98968	3.37	0.43	12.4	7	1	white	0	0.55312	0.44688
64	5.7	0.24	0.47	6.3	0.069	35.0	182.0	0.99391	3.11	0.46	9.75	5	0	white	0	0.55312	0.44688
65	5.7	0.25	0.27	10.8	0.05	58.0	116.0	0.99592	3.1	0.5	9.8	6	0	white	0	0.605757	0.394243
66	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0	0.55312	0.44688
67	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0	0.55312	0.44688
68	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	0	0.605757	0.394243
69	5.7	0.26	0.27	4.1	0.201	73.5	189.5	0.9942	3.27	0.38	9.4	6	0	white	0	0.585598	0.414402
70	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	0	0.55312	0.44688
71	5.7	0.28	0.28	2.2	0.019	15.0	65.0	0.9902	3.06	0.52	11.2	6	0	white	0	0.55312	0.44688
72	5.7	0.335	0.34	1.0	0.04	13.0	174.0	0.992	3.27	0.66	10.0	5	0	white	0	0.55312	0.44688
73	5.7	0.41	0.21	1.9	0.048	30.0	112.0	0.99138	3.29	0.55	11.2	6	0	white	0	0.585598	0.414402
74	5.7	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	0	0.55312	0.44688
75	5.7	1.13	0.09	1.5	0.172	7.0	19.0	0.994	3.5	0.48	9.8	4	0	red	0	0.585598	0.414402
76	5.8	0.13	0.22	12.7	0.058	24.0	183.0	0.9956	3.32	0.42	11.7	6	0	white	0	0.585598	0.414402
77	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	0	0.55312	0.44688
78	5.8	0.17	0.36	1.3	0.036	11.0	70.0	0.99202	3.43	0.68	10.4	7	1	white	0	0.55312	0.44688
79	5.8	0.2	0.34	1.0	0.035	40.0	86.0	0.98993	3.5	0.42	11.7	5	0	white	0	0.55312	0.44688
80	5.8	0.21	0.32	1.6	0.045	38.0	95.0	0.98946	3.23	0.94	12.4	8	1	white	0	0.55312	0.44688
81	5.8	0.23	0.2	2.0	0.043	39.0	154.0	0.99226	3.21	0.39	10.2	6	0	white	0	0.585598	0.414402
82	5.8	0.24	0.28	1.4	0.038	40.0	76.0	0.98711	3.1	0.29	13.9	7	1	white	0	0.55312	0.44688
83	5.8	0.24	0.39	1.5	0.054	37.0	158.0	0.9932	3.21	0.52	9.3	6	0	white	0	0.55312	0.44688
84	5.8	0.24	0.44	3.5	0.029	5.0	109.0	0.9913	3.53	0.43	11.7	3	0	white	0	0.55312	0.44688
85	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0	0.585598	0.414402
86	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0	0.585598	0.414402
87	5.8	0.27	0.2	7.3	0.04	42.0	145.0	0.99442	3.15	0.48	9.8	5	0	white	0	0.585598	0.414402
88	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	0	0.55312	0.44688
89	5.8	0.28	0.34	4.0	0.031	40.0	99.0	0.9896	3.39	0.39	12.8	7	1	white	0	0.55312	0.44688
90	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	0	0.595698	0.404302
91	5.8	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	0	0.585598	0.414402
92	5.8	0.29	0.15	1.1	0.029	12.0	83.0	0.9898	3.3	0.4	11.4	6	0	white	0	0.585598	0.414402
93	5.8	0.29	0.38	10.7	0.038	49.0	136.0	0.99366	3.11	0.59	11.2	6	0	white	0	0.55312	0.44688
94	5.8	0.31	0.31	7.5	0.052	55.0	230.0	0.9949	3.19	0.46	9.8	5	0	white	0	0.55312	0.44688
95	5.8	0.32	0.28	4.3	0.032	46.0	115.0	0.98946	3.16	0.57	13.0	8	1	white	0	0.55312	0.44688
96	5.8	0.34	0.21	6.6	0.04	50.0	167.0	0.9941	3.29	0.62	10.0	5	0	white	0	0.585598	0.414402
97	5.8	0.36	0.32	1.7	0.033	22.0	96.0	0.9898	3.03	0.38	11.2	6	0	white	0	0.55312	0.44688
98	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	0	0.585598	0.414402
99	5.8	0.4	0.42	4.4	0.047	38.5	245.0	0.9937	3.25	0.57	9.6	6	0	white	0	0.55312	0.44688
100	5.8	0.58	0.0	1.5	0.02	33.0	96.0	0.98918	3.29	0.38	12.4	6	0	white	0	0.585598	0.414402

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[4]: 
array([[1040,    0],
       [ 266,    0]])

Note

In classification, the cutoff is a threshold value used to determine class assignment based on predicted probabilities or scores from a classification model. In binary classification, if the predicted probability for a specific class is greater than or equal to the cutoff, the instance is assigned to the positive class; otherwise, it is assigned to the negative class. Adjusting the cutoff allows for trade-offs between true positives and false positives, enabling the model to be optimized for specific objectives or to consider the relative costs of different classification errors. The choice of cutoff is critical for tailoring the model’s performance to meet specific needs.

Main Plots (Classification Curves)

Classification models allow for the creation of various plots that are very helpful in understanding the model, such as the ROC Curve, PRC Curve, Cutoff Curve, Gain Curve, and more.

Most of the classification curves can be found in the Machine Learning - Classification Curve.

For example, let’s draw the model’s ROC curve.

model.roc_curve()

Important

Most of the curves have a parameter called nbins, which is essential for estimating metrics. The larger the nbins, the more precise the estimation, but it can significantly impact performance. Exercise caution when increasing this parameter excessively.

Hint

In binary classification, various curves can be easily plotted. However, in multi-class classification, it’s important to select the pos_label, representing the class to be treated as positive when drawing the curve.

Other Plots

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

model.plot_tree()

Note

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

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

model.to_graphviz()
Out[5]: 'digraph Tree {\ngraph [bgcolor="#FFFFFFDD"];\n0 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.995772", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.995772", color="#000000", fontcolor="#000000"]\n1 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 0.276667", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 0.276667", color="#000000", fontcolor="#000000"]\n2 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.276667", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.276667", color="#000000", fontcolor="#000000"]\n3 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#000000"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">logodds(0): 1.29</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -1.29</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n4 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#000000"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">logodds(0): 0.8</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -0.8</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n5 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#000000"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">logodds(0): 1.89</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -1.89</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n6 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#000000"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">logodds(0): 1.47</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -1.47</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n}'

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

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

model.contour()

Important

Machine learning models with two predictors can usually benefit from their own contour plot. This visual representation aids in exploring predictions and gaining a deeper understanding of how these models perform in different scenarios. Please refer to Contour Plot for more examples.

Parameter Modification

In order to see the parameters:

model.get_params()
Out[6]: 
{'max_ntree': 3,
 'max_depth': 3,
 'nbins': 6,
 'split_proposal_method': 'global',
 'tol': 0.001,
 'learning_rate': 0.1,
 'min_split_loss': 0,
 'weight_reg': 0,
 'sample': 0.7,
 'col_sample_by_tree': 1,
 'col_sample_by_node': 1}

And to manually change some of the parameters:

model.set_params({'max_depth': 5})

Model Register

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to Model Tracking and Versioning for more details on model tracking and versioning.

Model Exporting

To Memmodel

model.to_memmodel()

Note

MemModel objects serve as in-memory representations of machine learning models. They can be used for both in-database and in-memory prediction tasks. These objects can be pickled in the same way that you would pickle a scikit-learn model.

The preceding methods for exporting the model use MemModel, and it is recommended to use MemModel directly.

To SQL

You can get the SQL query equivalent of the XGB model by:

model.to_sql()
Out[8]: '(CASE WHEN (1 / (1 + EXP(- (-1.419371306498331 + 0.1 * ((CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.28877 ELSE -0.797495 END) ELSE (CASE WHEN "citric_acid" < 0.276667 THEN -1.8855 ELSE -1.46609 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.14543 ELSE -0.699181 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -1.24376 ELSE -1.56878 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.02379 ELSE -0.636177 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -1.16587 ELSE -1.49452 END) END)))))) / ((1 / (1 + EXP(- (1.419371306498331 + 0.1 * ((CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN 1.28877 ELSE 0.797495 END) ELSE (CASE WHEN "citric_acid" < 0.276667 THEN 1.8855 ELSE 1.46609 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN 1.14543 ELSE 0.699181 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN 1.24376 ELSE 1.56878 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN 1.02379 ELSE 0.636177 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN 1.16587 ELSE 1.49452 END) END)))))) + (1 / (1 + EXP(- (-1.419371306498331 + 0.1 * ((CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.28877 ELSE -0.797495 END) ELSE (CASE WHEN "citric_acid" < 0.276667 THEN -1.8855 ELSE -1.46609 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.14543 ELSE -0.699181 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -1.24376 ELSE -1.56878 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.02379 ELSE -0.636177 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -1.16587 ELSE -1.49452 END) END))))))) > 0.5 THEN 1 ELSE 0 END)'

Note

This SQL query can be directly used in any database.

Deploy SQL

To get the SQL query which uses Vertica functions use below:

model.deploySQL()
Out[9]: 'PREDICT_XGB_CLASSIFIER("fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density" USING PARAMETERS model_name = \'"public"."_verticapy_tmp_xgbclassifier_v_demo_deb55a7255a311ef880f0242ac120002_"\', match_by_pos = \'true\')'

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]

model.to_python()(X)
Out[11]: array([0])

Hint

The to_python() method is used to retrieve predictions, probabilities, or cluster distances. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

__init__(name: str = None, overwrite_model: bool = False, max_ntree: int = 10, max_depth: int = 5, nbins: int = 32, split_proposal_method: Literal['local', 'global'] = 'global', tol: float = 0.001, learning_rate: float = 0.1, min_split_loss: float = 0.0, weight_reg: float = 0.0, sample: float = 1.0, col_sample_by_tree: float = 1.0, col_sample_by_node: float = 1.0) → None

Must be overridden in the child class

Methods

__init__([name, overwrite_model, max_ntree, ...])	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, pos_label, cutoff, allSQL])	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()	Drops the model from the Vertica database.
export_models(name, path[, kind])	Exports machine learning models.
features_importance([tree_id, show, chart])	Computes the model's features importance.
fit(input_relation, X, y[, test_relation, ...])	Trains the model.
get_attributes([attr_name])	Returns the model attributes.
get_match_index(x, col_list[, str_check])	Returns the matching index.
get_params()	Returns the parameters of the model.
get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
get_score([tree_id])	Returns the feature importance metrics for the input tree.
get_tree([tree_id])	Returns a table with all the input tree information.
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.
plot([max_nb_points, chart])	Draws the model.
plot_tree([tree_id, pic_path])	Draws the input tree.
prc_curve([pos_label, nbins, show, chart])	Draws the model PRC curve.
predict(vdf[, X, name, cutoff, inplace])	Predicts using the input relation.
predict_proba(vdf[, X, name, pos_label, inplace])	Returns the model's probabilities using the input relation.
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_graphviz([tree_id, classes_color, ...])	Returns the code for a Graphviz tree.
to_json([path])	Creates a Python XGBoost JSON file
to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
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