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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	3.9	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.

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_mldb_7d8b2786979811efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_7dba7450979811efa8720242ac120002_"', '"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|5200 

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	0.6544676806083651
prc_auc	0.48533555468295325
accuracy	0.8111025443330764
log_loss	0.259887085975558
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.18889745566692362
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	0.6544676806083651
prc_auc	0.48533555468295325
accuracy	0.18889745566692367
log_loss	0.259887085975558
precision	0.18889745566692367
recall	1.0
f1_score	0.317769130998703
mcc	0.0
informedness	0.0
markedness	-0.8111025443330764
csi	0.18889745566692367

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]: 0.8111025443330764

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	4.8	0.225	0.38	1.2	0.074	47.0	130.0	0.99132	3.31	0.4	10.3	6	0	white	0
3	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	0
4	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	0
5	5.2	0.37	0.33	1.2	0.028	13.0	81.0	0.9902	3.37	0.38	11.7	6	0	white	0
6	5.3	0.21	0.29	0.7	0.028	11.0	66.0	0.99215	3.3	0.4	9.8	5	0	white	0
7	5.4	0.22	0.35	6.5	0.029	26.0	87.0	0.99092	3.29	0.44	12.5	7	1	white	0
8	5.4	0.33	0.31	4.0	0.03	27.0	108.0	0.99031	3.3	0.43	12.2	7	1	white	0
9	5.5	0.34	0.26	2.2	0.021	31.0	119.0	0.98919	3.55	0.49	13.0	8	1	white	0
10	5.5	0.49	0.03	1.8	0.044	28.0	87.0	0.9908	3.5	0.82	14.0	8	1	red	0
11	5.6	0.12	0.33	2.9	0.044	21.0	73.0	0.98896	3.17	0.32	12.9	8	1	white	0
12	5.6	0.19	0.39	1.1	0.043	17.0	67.0	0.9918	3.23	0.53	10.3	6	0	white	0
13	5.6	0.24	0.34	2.0	0.041	14.0	73.0	0.98981	3.04	0.45	11.6	7	1	white	0
14	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
15	5.6	0.615	0.0	1.6	0.089	16.0	59.0	0.9943	3.58	0.52	9.9	5	0	red	0
16	5.7	0.15	0.28	3.7	0.045	57.0	151.0	0.9913	3.22	0.27	11.2	6	0	white	0
17	5.7	0.16	0.32	1.2	0.036	7.0	89.0	0.99111	3.26	0.48	11.0	5	0	white	0
18	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
19	5.7	0.27	0.16	9.0	0.053	32.0	111.0	0.99474	3.36	0.37	10.4	6	0	white	0
20	5.7	0.44	0.13	7.0	0.025	28.0	173.0	0.9913	3.33	0.48	12.5	6	0	white	0
21	5.8	0.18	0.28	1.3	0.034	9.0	94.0	0.99092	3.21	0.52	11.2	6	0	white	0
22	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
23	5.8	0.275	0.3	5.4	0.043	41.0	149.0	0.9926	3.33	0.42	10.8	7	1	white	0
24	5.8	0.34	0.21	7.2	0.041	48.0	146.0	0.99441	3.16	0.49	9.8	5	0	white	0
25	5.8	0.61	0.11	1.8	0.066	18.0	28.0	0.99483	3.55	0.66	10.9	6	0	red	0
26	5.9	0.12	0.27	4.8	0.03	40.0	110.0	0.99226	3.55	0.68	12.1	6	0	white	0
27	5.9	0.24	0.12	1.4	0.035	60.0	247.0	0.99358	3.34	0.44	9.6	6	0	white	0
28	5.9	0.26	0.21	12.5	0.034	36.0	152.0	0.9972	3.28	0.43	9.5	6	0	white	0
29	5.9	0.48	0.3	1.5	0.037	19.0	78.0	0.99057	3.47	0.42	11.9	7	1	white	0
30	5.9	0.61	0.08	2.1	0.071	16.0	24.0	0.99376	3.56	0.77	11.1	6	0	red	0
31	6.0	0.13	0.28	5.7	0.038	56.0	189.5	0.9948	3.59	0.43	10.6	7	1	white	0
32	6.0	0.19	0.26	1.4	0.039	30.0	104.0	0.98998	3.32	0.41	12.4	6	0	white	0
33	6.0	0.22	0.28	1.1	0.034	47.0	90.0	0.98862	3.22	0.38	12.6	6	0	white	0
34	6.0	0.22	0.28	1.1	0.034	47.0	90.0	0.98862	3.22	0.38	12.6	6	0	white	0
35	6.0	0.26	0.29	1.0	0.032	27.0	96.0	0.9896	3.38	0.44	12.3	6	0	white	0
36	6.0	0.27	0.15	1.5	0.056	35.0	128.0	0.9936	3.12	0.45	8.8	5	0	white	0
37	6.0	0.28	0.25	1.8	0.042	8.0	108.0	0.9929	3.08	0.55	9.0	5	0	white	0
38	6.0	0.28	0.49	6.8	0.048	61.0	222.0	0.9953	3.19	0.47	9.3	5	0	white	0
39	6.0	0.29	0.25	1.4	0.033	30.0	114.0	0.98794	3.08	0.43	13.2	6	0	white	0
40	6.0	0.29	0.41	10.8	0.048	55.0	149.0	0.9937	3.09	0.59	10.9666666666667	7	1	white	0
41	6.0	0.31	0.47	3.6	0.067	18.0	42.0	0.99549	3.39	0.66	11.0	6	0	red	0
42	6.0	0.495	0.27	5.0	0.157	17.0	129.0	0.99396	3.03	0.36	9.3	5	0	white	0
43	6.0	0.5	0.0	1.4	0.057	15.0	26.0	0.99448	3.36	0.45	9.5	5	0	red	0
44	6.0	0.64	0.05	1.9	0.066	9.0	17.0	0.99496	3.52	0.78	10.6	5	0	red	0
45	6.1	0.2	0.17	1.6	0.048	46.0	129.0	0.991	3.3	0.43	11.4	6	0	white	0
46	6.1	0.21	0.38	1.5	0.039	37.0	122.0	0.98972	3.2	0.43	12.0	6	0	white	0
47	6.1	0.22	0.28	16.55	0.059	54.0	135.0	0.99665	3.2	0.38	10.5	5	0	white	0
48	6.1	0.27	0.44	6.7	0.041	61.0	230.0	0.99505	3.12	0.4	8.9	5	0	white	0
49	6.1	0.28	0.27	4.7	0.03	56.0	140.0	0.99042	3.16	0.42	12.5	8	1	white	0
50	6.1	0.28	0.27	4.7	0.03	56.0	140.0	0.99042	3.16	0.42	12.5	8	1	white	0
51	6.1	0.31	0.34	2.8	0.042	59.5	162.0	0.99179	3.27	0.47	10.8	6	0	white	0
52	6.1	0.32	0.25	2.3	0.071	23.0	58.0	0.99633	3.42	0.97	10.6	5	0	red	0
53	6.1	0.32	0.33	10.7	0.036	27.0	98.0	0.99521	3.34	0.52	10.2	6	0	white	0
54	6.1	0.34	0.31	12.0	0.053	46.0	238.0	0.9977	3.16	0.48	8.6	5	0	white	0
55	6.1	0.4	0.31	0.9	0.048	23.0	170.0	0.993	3.22	0.77	9.5	6	0	white	0
56	6.2	0.22	0.27	1.5	0.064	20.0	132.0	0.9938	3.22	0.46	9.2	6	0	white	0
57	6.2	0.26	0.2	8.0	0.047	35.0	111.0	0.99445	3.11	0.42	10.4	6	0	white	0
58	6.2	0.28	0.43	13.0	0.039	64.0	233.0	0.99745	3.08	0.38	8.9	5	0	white	0
59	6.2	0.31	0.23	3.3	0.052	34.0	113.0	0.99429	3.16	0.48	8.4	5	0	white	0
60	6.2	0.32	0.24	4.1	0.051	34.0	149.0	0.99306	3.36	0.52	11.0	5	0	white	0
61	6.2	0.345	0.27	10.1	0.056	38.0	187.0	0.99486	3.31	0.56	10.6	5	0	white	0
62	6.2	0.35	0.25	18.4	0.051	28.0	182.0	0.99946	3.13	0.62	9.3	6	0	white	0
63	6.3	0.27	0.38	0.9	0.051	7.0	140.0	0.9926	3.45	0.5	10.5	7	1	white	0
64	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white	0
65	6.3	0.28	0.24	8.45	0.031	32.0	172.0	0.9958	3.39	0.57	9.7	7	1	white	0
66	6.3	0.3	0.48	7.4	0.053	34.0	149.0	0.99472	3.18	0.53	9.8	5	0	white	0
67	6.3	0.31	0.34	2.2	0.045	20.0	77.0	0.9927	3.3	0.43	10.2	5	0	white	0
68	6.3	0.32	0.17	17.75	0.06	51.0	190.0	0.99916	3.13	0.48	8.8	6	0	white	0
69	6.3	0.34	0.33	4.6	0.034	19.0	80.0	0.9917	3.38	0.58	12.0	7	1	white	0
70	6.3	0.36	0.3	4.8	0.049	14.0	85.0	0.9932	3.28	0.39	10.6	5	0	white	0
71	6.4	0.105	0.29	1.1	0.035	44.0	140.0	0.99142	3.17	0.55	10.7	7	1	white	0
72	6.4	0.21	0.21	5.1	0.097	21.0	105.0	0.9939	3.07	0.46	9.6	5	0	white	0
73	6.4	0.21	0.28	5.9	0.047	29.0	101.0	0.99278	3.15	0.4	11.0	6	0	white	0
74	6.4	0.22	0.34	1.8	0.057	29.0	104.0	0.9959	3.81	0.57	10.3	6	0	white	0
75	6.4	0.22	0.49	7.5	0.054	42.0	151.0	0.9948	3.27	0.52	10.1	6	0	white	0
76	6.4	0.23	0.33	1.15	0.044	15.5	217.5	0.992	3.33	0.44	11.0	6	0	white	0
77	6.4	0.24	0.26	8.2	0.054	47.0	182.0	0.99538	3.12	0.5	9.5	5	0	white	0
78	6.4	0.24	0.27	1.5	0.04	35.0	105.0	0.98914	3.13	0.3	12.4	6	0	white	0
79	6.4	0.25	0.57	1.0	0.062	21.0	122.0	0.99238	3.0	0.4	9.5	5	0	white	0
80	6.4	0.25	0.57	1.0	0.062	21.0	122.0	0.99238	3.0	0.4	9.5	5	0	white	0
81	6.4	0.25	0.74	7.8	0.045	52.0	209.0	0.9956	3.21	0.42	9.2	6	0	white	0
82	6.4	0.28	0.28	3.0	0.04	19.0	98.0	0.99216	3.25	0.47	11.1	6	0	white	0
83	6.4	0.31	0.26	13.2	0.046	57.0	205.0	0.9975	3.17	0.41	9.6	5	0	white	0
84	6.4	0.31	0.31	12.9	0.045	55.0	161.0	0.99546	3.02	0.59	10.2	5	0	white	0
85	6.4	0.31	0.31	12.9	0.045	55.0	161.0	0.99546	3.02	0.59	10.2	5	0	white	0
86	6.4	0.36	0.53	2.2	0.23	19.0	35.0	0.9934	3.37	0.93	12.4	6	0	red	0
87	6.4	0.38	0.24	7.2	0.047	41.0	151.0	0.99604	3.11	0.6	9.2	5	0	white	0
88	6.4	0.79	0.04	2.2	0.061	11.0	17.0	0.99588	3.53	0.65	10.4	6	0	red	0
89	6.5	0.14	0.32	2.7	0.037	18.0	89.0	0.9924	3.4	0.74	11.5	7	1	white	0
90	6.5	0.15	0.44	12.6	0.052	65.0	158.0	0.99688	3.26	0.7	10.3	7	1	white	0
91	6.5	0.29	0.52	7.9	0.049	35.0	192.0	0.99551	3.16	0.51	9.5	6	0	white	0
92	6.5	0.33	0.24	14.5	0.048	20.0	96.0	0.99456	3.06	0.3	11.5	8	1	white	0
93	6.5	0.35	0.31	10.2	0.069	58.0	170.0	0.99692	3.18	0.49	9.4	5	0	white	0
94	6.5	0.4	0.1	2.0	0.076	30.0	47.0	0.99554	3.36	0.48	9.4	6	0	red	0
95	6.5	0.61	0.0	2.2	0.095	48.0	59.0	0.99541	3.61	0.7	11.5	6	0	red	0
96	6.6	0.21	0.29	5.35	0.029	43.0	106.0	0.99112	2.93	0.43	11.5	7	1	white	0
97	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white	0
98	6.6	0.23	0.29	14.45	0.057	29.0	144.0	0.99756	3.33	0.54	10.2	6	0	white	0
99	6.6	0.25	0.35	14.0	0.069	42.0	163.0	0.999	3.56	0.47	9.8	5	0	white	0
100	6.6	0.25	0.41	7.4	0.043	29.0	151.0	0.9946	3.15	0.6	10.2	7	1	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	0.551095	0.448905
2	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	0.551095	0.448905
3	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	0	0.586944	0.413056
4	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	0	0.586944	0.413056
5	5.2	0.37	0.33	1.2	0.028	13.0	81.0	0.9902	3.37	0.38	11.7	6	0	white	0	0.551095	0.448905
6	5.3	0.21	0.29	0.7	0.028	11.0	66.0	0.99215	3.3	0.4	9.8	5	0	white	0	0.551095	0.448905
7	5.4	0.22	0.35	6.5	0.029	26.0	87.0	0.99092	3.29	0.44	12.5	7	1	white	0	0.551095	0.448905
8	5.4	0.33	0.31	4.0	0.03	27.0	108.0	0.99031	3.3	0.43	12.2	7	1	white	0	0.551095	0.448905
9	5.5	0.34	0.26	2.2	0.021	31.0	119.0	0.98919	3.55	0.49	13.0	8	1	white	0	0.586944	0.413056
10	5.5	0.49	0.03	1.8	0.044	28.0	87.0	0.9908	3.5	0.82	14.0	8	1	red	0	0.586944	0.413056
11	5.6	0.12	0.33	2.9	0.044	21.0	73.0	0.98896	3.17	0.32	12.9	8	1	white	0	0.551095	0.448905
12	5.6	0.19	0.39	1.1	0.043	17.0	67.0	0.9918	3.23	0.53	10.3	6	0	white	0	0.551095	0.448905
13	5.6	0.24	0.34	2.0	0.041	14.0	73.0	0.98981	3.04	0.45	11.6	7	1	white	0	0.551095	0.448905
14	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	0.586944	0.413056
15	5.6	0.615	0.0	1.6	0.089	16.0	59.0	0.9943	3.58	0.52	9.9	5	0	red	0	0.586944	0.413056
16	5.7	0.15	0.28	3.7	0.045	57.0	151.0	0.9913	3.22	0.27	11.2	6	0	white	0	0.551095	0.448905
17	5.7	0.16	0.32	1.2	0.036	7.0	89.0	0.99111	3.26	0.48	11.0	5	0	white	0	0.551095	0.448905
18	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	0.551095	0.448905
19	5.7	0.27	0.16	9.0	0.053	32.0	111.0	0.99474	3.36	0.37	10.4	6	0	white	0	0.586944	0.413056
20	5.7	0.44	0.13	7.0	0.025	28.0	173.0	0.9913	3.33	0.48	12.5	6	0	white	0	0.586944	0.413056
21	5.8	0.18	0.28	1.3	0.034	9.0	94.0	0.99092	3.21	0.52	11.2	6	0	white	0	0.551095	0.448905
22	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	0.586944	0.413056
23	5.8	0.275	0.3	5.4	0.043	41.0	149.0	0.9926	3.33	0.42	10.8	7	1	white	0	0.551095	0.448905
24	5.8	0.34	0.21	7.2	0.041	48.0	146.0	0.99441	3.16	0.49	9.8	5	0	white	0	0.586944	0.413056
25	5.8	0.61	0.11	1.8	0.066	18.0	28.0	0.99483	3.55	0.66	10.9	6	0	red	0	0.586944	0.413056
26	5.9	0.12	0.27	4.8	0.03	40.0	110.0	0.99226	3.55	0.68	12.1	6	0	white	0	0.586944	0.413056
27	5.9	0.24	0.12	1.4	0.035	60.0	247.0	0.99358	3.34	0.44	9.6	6	0	white	0	0.586944	0.413056
28	5.9	0.26	0.21	12.5	0.034	36.0	152.0	0.9972	3.28	0.43	9.5	6	0	white	0	0.609113	0.390887
29	5.9	0.48	0.3	1.5	0.037	19.0	78.0	0.99057	3.47	0.42	11.9	7	1	white	0	0.551095	0.448905
30	5.9	0.61	0.08	2.1	0.071	16.0	24.0	0.99376	3.56	0.77	11.1	6	0	red	0	0.586944	0.413056
31	6.0	0.13	0.28	5.7	0.038	56.0	189.5	0.9948	3.59	0.43	10.6	7	1	white	0	0.551095	0.448905
32	6.0	0.19	0.26	1.4	0.039	30.0	104.0	0.98998	3.32	0.41	12.4	6	0	white	0	0.586944	0.413056
33	6.0	0.22	0.28	1.1	0.034	47.0	90.0	0.98862	3.22	0.38	12.6	6	0	white	0	0.551095	0.448905
34	6.0	0.22	0.28	1.1	0.034	47.0	90.0	0.98862	3.22	0.38	12.6	6	0	white	0	0.551095	0.448905
35	6.0	0.26	0.29	1.0	0.032	27.0	96.0	0.9896	3.38	0.44	12.3	6	0	white	0	0.551095	0.448905
36	6.0	0.27	0.15	1.5	0.056	35.0	128.0	0.9936	3.12	0.45	8.8	5	0	white	0	0.586944	0.413056
37	6.0	0.28	0.25	1.8	0.042	8.0	108.0	0.9929	3.08	0.55	9.0	5	0	white	0	0.586944	0.413056
38	6.0	0.28	0.49	6.8	0.048	61.0	222.0	0.9953	3.19	0.47	9.3	5	0	white	0	0.551095	0.448905
39	6.0	0.29	0.25	1.4	0.033	30.0	114.0	0.98794	3.08	0.43	13.2	6	0	white	0	0.586944	0.413056
40	6.0	0.29	0.41	10.8	0.048	55.0	149.0	0.9937	3.09	0.59	10.9666666666667	7	1	white	0	0.551095	0.448905
41	6.0	0.31	0.47	3.6	0.067	18.0	42.0	0.99549	3.39	0.66	11.0	6	0	red	0	0.551095	0.448905
42	6.0	0.495	0.27	5.0	0.157	17.0	129.0	0.99396	3.03	0.36	9.3	5	0	white	0	0.586944	0.413056
43	6.0	0.5	0.0	1.4	0.057	15.0	26.0	0.99448	3.36	0.45	9.5	5	0	red	0	0.586944	0.413056
44	6.0	0.64	0.05	1.9	0.066	9.0	17.0	0.99496	3.52	0.78	10.6	5	0	red	0	0.586944	0.413056
45	6.1	0.2	0.17	1.6	0.048	46.0	129.0	0.991	3.3	0.43	11.4	6	0	white	0	0.586944	0.413056
46	6.1	0.21	0.38	1.5	0.039	37.0	122.0	0.98972	3.2	0.43	12.0	6	0	white	0	0.551095	0.448905
47	6.1	0.22	0.28	16.55	0.059	54.0	135.0	0.99665	3.2	0.38	10.5	5	0	white	0	0.59893	0.40107
48	6.1	0.27	0.44	6.7	0.041	61.0	230.0	0.99505	3.12	0.4	8.9	5	0	white	0	0.551095	0.448905
49	6.1	0.28	0.27	4.7	0.03	56.0	140.0	0.99042	3.16	0.42	12.5	8	1	white	0	0.586944	0.413056
50	6.1	0.28	0.27	4.7	0.03	56.0	140.0	0.99042	3.16	0.42	12.5	8	1	white	0	0.586944	0.413056
51	6.1	0.31	0.34	2.8	0.042	59.5	162.0	0.99179	3.27	0.47	10.8	6	0	white	0	0.551095	0.448905
52	6.1	0.32	0.25	2.3	0.071	23.0	58.0	0.99633	3.42	0.97	10.6	5	0	red	0	0.619575	0.380425
53	6.1	0.32	0.33	10.7	0.036	27.0	98.0	0.99521	3.34	0.52	10.2	6	0	white	0	0.551095	0.448905
54	6.1	0.34	0.31	12.0	0.053	46.0	238.0	0.9977	3.16	0.48	8.6	5	0	white	0	0.60949	0.39051
55	6.1	0.4	0.31	0.9	0.048	23.0	170.0	0.993	3.22	0.77	9.5	6	0	white	0	0.551095	0.448905
56	6.2	0.22	0.27	1.5	0.064	20.0	132.0	0.9938	3.22	0.46	9.2	6	0	white	0	0.586944	0.413056
57	6.2	0.26	0.2	8.0	0.047	35.0	111.0	0.99445	3.11	0.42	10.4	6	0	white	0	0.586944	0.413056
58	6.2	0.28	0.43	13.0	0.039	64.0	233.0	0.99745	3.08	0.38	8.9	5	0	white	0	0.59893	0.40107
59	6.2	0.31	0.23	3.3	0.052	34.0	113.0	0.99429	3.16	0.48	8.4	5	0	white	0	0.586944	0.413056
60	6.2	0.32	0.24	4.1	0.051	34.0	149.0	0.99306	3.36	0.52	11.0	5	0	white	0	0.586944	0.413056
61	6.2	0.345	0.27	10.1	0.056	38.0	187.0	0.99486	3.31	0.56	10.6	5	0	white	0	0.586944	0.413056
62	6.2	0.35	0.25	18.4	0.051	28.0	182.0	0.99946	3.13	0.62	9.3	6	0	white	0	0.619575	0.380425
63	6.3	0.27	0.38	0.9	0.051	7.0	140.0	0.9926	3.45	0.5	10.5	7	1	white	0	0.551095	0.448905
64	6.3	0.27	0.49	1.2	0.063	35.0	92.0	0.9911	3.38	0.42	12.2	6	0	white	0	0.551095	0.448905
65	6.3	0.28	0.24	8.45	0.031	32.0	172.0	0.9958	3.39	0.57	9.7	7	1	white	0	0.609113	0.390887
66	6.3	0.3	0.48	7.4	0.053	34.0	149.0	0.99472	3.18	0.53	9.8	5	0	white	0	0.551095	0.448905
67	6.3	0.31	0.34	2.2	0.045	20.0	77.0	0.9927	3.3	0.43	10.2	5	0	white	0	0.551095	0.448905
68	6.3	0.32	0.17	17.75	0.06	51.0	190.0	0.99916	3.13	0.48	8.8	6	0	white	0	0.619575	0.380425
69	6.3	0.34	0.33	4.6	0.034	19.0	80.0	0.9917	3.38	0.58	12.0	7	1	white	0	0.551095	0.448905
70	6.3	0.36	0.3	4.8	0.049	14.0	85.0	0.9932	3.28	0.39	10.6	5	0	white	0	0.551095	0.448905
71	6.4	0.105	0.29	1.1	0.035	44.0	140.0	0.99142	3.17	0.55	10.7	7	1	white	0	0.551095	0.448905
72	6.4	0.21	0.21	5.1	0.097	21.0	105.0	0.9939	3.07	0.46	9.6	5	0	white	0	0.586944	0.413056
73	6.4	0.21	0.28	5.9	0.047	29.0	101.0	0.99278	3.15	0.4	11.0	6	0	white	0	0.551095	0.448905
74	6.4	0.22	0.34	1.8	0.057	29.0	104.0	0.9959	3.81	0.57	10.3	6	0	white	0	0.59893	0.40107
75	6.4	0.22	0.49	7.5	0.054	42.0	151.0	0.9948	3.27	0.52	10.1	6	0	white	0	0.551095	0.448905
76	6.4	0.23	0.33	1.15	0.044	15.5	217.5	0.992	3.33	0.44	11.0	6	0	white	0	0.551095	0.448905
77	6.4	0.24	0.26	8.2	0.054	47.0	182.0	0.99538	3.12	0.5	9.5	5	0	white	0	0.586944	0.413056
78	6.4	0.24	0.27	1.5	0.04	35.0	105.0	0.98914	3.13	0.3	12.4	6	0	white	0	0.586944	0.413056
79	6.4	0.25	0.57	1.0	0.062	21.0	122.0	0.99238	3.0	0.4	9.5	5	0	white	0	0.551095	0.448905
80	6.4	0.25	0.57	1.0	0.062	21.0	122.0	0.99238	3.0	0.4	9.5	5	0	white	0	0.551095	0.448905
81	6.4	0.25	0.74	7.8	0.045	52.0	209.0	0.9956	3.21	0.42	9.2	6	0	white	0	0.551095	0.448905
82	6.4	0.28	0.28	3.0	0.04	19.0	98.0	0.99216	3.25	0.47	11.1	6	0	white	0	0.551095	0.448905
83	6.4	0.31	0.26	13.2	0.046	57.0	205.0	0.9975	3.17	0.41	9.6	5	0	white	0	0.619575	0.380425
84	6.4	0.31	0.31	12.9	0.045	55.0	161.0	0.99546	3.02	0.59	10.2	5	0	white	0	0.551095	0.448905
85	6.4	0.31	0.31	12.9	0.045	55.0	161.0	0.99546	3.02	0.59	10.2	5	0	white	0	0.551095	0.448905
86	6.4	0.36	0.53	2.2	0.23	19.0	35.0	0.9934	3.37	0.93	12.4	6	0	red	0	0.551095	0.448905
87	6.4	0.38	0.24	7.2	0.047	41.0	151.0	0.99604	3.11	0.6	9.2	5	0	white	0	0.619575	0.380425
88	6.4	0.79	0.04	2.2	0.061	11.0	17.0	0.99588	3.53	0.65	10.4	6	0	red	0	0.619575	0.380425
89	6.5	0.14	0.32	2.7	0.037	18.0	89.0	0.9924	3.4	0.74	11.5	7	1	white	0	0.551095	0.448905
90	6.5	0.15	0.44	12.6	0.052	65.0	158.0	0.99688	3.26	0.7	10.3	7	1	white	0	0.59893	0.40107
91	6.5	0.29	0.52	7.9	0.049	35.0	192.0	0.99551	3.16	0.51	9.5	6	0	white	0	0.551095	0.448905
92	6.5	0.33	0.24	14.5	0.048	20.0	96.0	0.99456	3.06	0.3	11.5	8	1	white	0	0.586944	0.413056
93	6.5	0.35	0.31	10.2	0.069	58.0	170.0	0.99692	3.18	0.49	9.4	5	0	white	0	0.60949	0.39051
94	6.5	0.4	0.1	2.0	0.076	30.0	47.0	0.99554	3.36	0.48	9.4	6	0	red	0	0.586944	0.413056
95	6.5	0.61	0.0	2.2	0.095	48.0	59.0	0.99541	3.61	0.7	11.5	6	0	red	0	0.586944	0.413056
96	6.6	0.21	0.29	5.35	0.029	43.0	106.0	0.99112	2.93	0.43	11.5	7	1	white	0	0.551095	0.448905
97	6.6	0.23	0.26	1.3	0.045	16.0	128.0	0.9934	3.36	0.6	10.0	6	0	white	0	0.586944	0.413056
98	6.6	0.23	0.29	14.45	0.057	29.0	144.0	0.99756	3.33	0.54	10.2	6	0	white	0	0.59893	0.40107
99	6.6	0.25	0.35	14.0	0.069	42.0	163.0	0.999	3.56	0.47	9.8	5	0	white	0	0.59893	0.40107
100	6.6	0.25	0.41	7.4	0.043	29.0	151.0	0.9946	3.15	0.6	10.2	7	1	white	0	0.551095	0.448905

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([[1052,    0],
       [ 245,    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="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.288333", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.288333", 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): 1.28</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -1.28</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): 0.78</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -0.78</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): 1.32</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -1.32</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): 1.76</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">logodds(1): -1.76</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 /notebooks/ml/model_tracking_versioning/index.ipynb 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(- (-1.3959376373916976 + 0.1 * ((CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.28077 ELSE -0.776 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -1.3165 ELSE -1.7581 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.14298 ELSE -0.639964 END) ELSE (CASE WHEN "citric_acid" < 0.276667 THEN -1.69394 ELSE -1.2682 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.08972 ELSE -0.635001 END) ELSE (CASE WHEN "fixed_acidity" < 9.85 THEN -1.4254 ELSE -0.825923 END) END)))))) / ((1 / (1 + EXP(- (1.3959376373916976 + 0.1 * ((CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN 1.28077 ELSE 0.776 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN 1.3165 ELSE 1.7581 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN 1.14298 ELSE 0.639964 END) ELSE (CASE WHEN "citric_acid" < 0.276667 THEN 1.69394 ELSE 1.2682 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN 1.08972 ELSE 0.635001 END) ELSE (CASE WHEN "fixed_acidity" < 9.85 THEN 1.4254 ELSE 0.825923 END) END)))))) + (1 / (1 + EXP(- (-1.3959376373916976 + 0.1 * ((CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.28077 ELSE -0.776 END) ELSE (CASE WHEN "volatile_acidity" < 0.288333 THEN -1.3165 ELSE -1.7581 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.14298 ELSE -0.639964 END) ELSE (CASE WHEN "citric_acid" < 0.276667 THEN -1.69394 ELSE -1.2682 END) END) + (CASE WHEN "density" < 0.995772 THEN (CASE WHEN "citric_acid" < 0.276667 THEN -1.08972 ELSE -0.635001 END) ELSE (CASE WHEN "fixed_acidity" < 9.85 THEN -1.4254 ELSE -0.825923 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_mldb_7d8b2786979811efa8720242ac120002_"\', 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