verticapy.machine_learning.vertica.tree.DecisionTreeClassifier¶

class verticapy.machine_learning.vertica.tree.DecisionTreeClassifier(name: str = None, overwrite_model: bool = False, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32)¶

A DecisionTreeClassifier consisting of a single tree.

Parameters¶

name: str, optional

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

overwrite_model: bool, optional

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

max_features: str / int, optional

The number of randomly chosen features from which to pick the best feature to split on a given tree node. It can be an integer or one of the two following methods.

auto:
square root of the total number of predictors.
max:
number of predictors.

max_leaf_nodes: PythonNumber, optional

The maximum number of leaf nodes for a tree in the forest, an integer between 1 and 1e9, inclusive.

max_depth: int, optional

The maximum depth for growing each tree, an integer between 1 and 100, inclusive.

min_samples_leaf: int, optional

The minimum number of samples each branch must have after a node is split, an integer between 1 and 1e6, inclusive. Any split that results in fewer remaining samples is discarded.

min_info_gain: PythonNumber, optional

The minimum threshold for including a split, a float between 0.0 and 1.0, inclusive. A split with information gain less than this threshold is discarded.

nbins: int, optional

The number of bins to use for continuous features, an integer between 2 and 1000, inclusive.

Attributes¶

Many attributes are created during the fitting phase.

trees_: list of one BinaryTreeClassifier: One tree model which is instance of BinaryTreeClassifier. It possess 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 MDI (Mean Decreased Impurity). 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.
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 RandomForest 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 DecisionTreeClassifier model:

from verticapy.machine_learning.vertica import DecisionTreeClassifier

Then we can create the model:

model = DecisionTreeClassifier(
    max_features = "auto",
    max_leaf_nodes = 32,
    max_depth = 3,
    min_samples_leaf = 5,
    min_info_gain = 0.0,
    nbins = 32,
)

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
===========
SELECT rf_classifier('"public"."_verticapy_tmp_randomforestclassifier_v_demo_fb75e43c55a411ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_fb854a2655a411ef880f0242ac120002_"', 'good', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', ntree=1, mtry=3, sampling_size=1, max_depth=3, max_breadth=32, min_leaf_size=5, min_info_gain=0, nbins=32);

=======
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


===============
Additional Info
===============
       Name       |Value
------------------+-----
    tree_count    |  1  
rejected_row_count|  0  
accepted_row_count|5195 

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 RandomForest, feature importance is calculated using the MDI (Mean Decreased Impurity). 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	0.7468945232673556
prc_auc	0.46441582138894216
accuracy	0.8033794162826421
log_loss	0.191153481288304
precision	0.5454545454545454
recall	0.18181818181818182
f1_score	0.2727272727272727
mcc	0.22947914410412062
informedness	0.1432825363461201
markedness	0.3675303279916129
csi	0.15789473684210525

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	0.7468945232673556
prc_auc	0.46441582138894216
accuracy	0.7204301075268817
log_loss	0.191153481288304
precision	0.38095238095238093
recall	0.6060606060606061
f1_score	0.4678362573099415
mcc	0.3058283188048218
informedness	0.35557891049220536
markedness	0.2630385487528346
csi	0.3053435114503817

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]: 0.8033794162826421

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	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	0
2	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	0
3	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	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	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white	0
6	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	0
7	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	1
8	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	0
9	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	1
10	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	0
11	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	0
12	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	1
13	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	1
14	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	0
15	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	0
16	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
17	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	1
18	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	0
19	5.2	0.25	0.23	1.4	0.047	20.0	77.0	0.99001	3.32	0.62	11.4	5	0	white	0
20	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	1
21	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	0
22	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	0
23	5.2	0.49	0.26	2.3	0.09	23.0	74.0	0.9953	3.71	0.62	12.2	6	0	red	0
24	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	0
25	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	0
26	5.3	0.24	0.33	1.3	0.033	25.0	97.0	0.9906	3.59	0.38	11.0	8	1	white	0
27	5.3	0.3	0.2	1.1	0.077	48.0	166.0	0.9944	3.3	0.54	8.7	4	0	white	0
28	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	1
29	5.3	0.57	0.01	1.7	0.054	5.0	27.0	0.9934	3.57	0.84	12.5	7	1	red	0
30	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	0
31	5.4	0.15	0.32	2.5	0.037	10.0	51.0	0.98878	3.04	0.58	12.6	6	0	white	1
32	5.4	0.17	0.27	2.7	0.049	28.0	104.0	0.99224	3.46	0.55	10.3	6	0	white	0
33	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
34	5.4	0.23	0.36	1.5	0.03	74.0	121.0	0.98976	3.24	0.99	12.1	7	1	white	1
35	5.4	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	6	0	white	0
36	5.4	0.27	0.22	4.6	0.022	29.0	107.0	0.98889	3.33	0.54	13.8	6	0	white	1
37	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	1
38	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	0
39	5.4	0.835	0.08	1.2	0.046	13.0	93.0	0.9924	3.57	0.85	13.0	7	1	red	0
40	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	0
41	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0
42	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	0
43	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
44	5.5	0.28	0.21	1.6	0.032	23.0	85.0	0.99027	3.42	0.42	12.5	5	0	white	1
45	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	1
46	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	1
47	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	0
48	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	0
49	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	0
50	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	1
51	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
52	5.6	0.2	0.66	10.2	0.043	78.0	175.0	0.9945	2.98	0.43	10.4	7	1	white	0
53	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
54	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
55	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
56	5.6	0.26	0.27	10.6	0.03	27.0	119.0	0.9947	3.4	0.34	10.7	7	1	white	0
57	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white	0
58	5.6	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	0
59	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
60	5.6	0.33	0.28	1.2	0.031	33.0	97.0	0.99126	3.49	0.58	10.9	6	0	white	0
61	5.6	0.35	0.37	1.0	0.038	6.0	72.0	0.9902	3.37	0.34	11.4	5	0	white	1
62	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white	0
63	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
64	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
65	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	0
66	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
67	5.6	0.66	0.0	2.5	0.066	7.0	15.0	0.99256	3.52	0.58	12.9	5	0	red	0
68	5.7	0.1	0.27	1.3	0.047	21.0	100.0	0.9928	3.27	0.46	9.5	5	0	white	0
69	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	0
70	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	0
71	5.7	0.22	0.28	1.3	0.027	26.0	101.0	0.98948	3.35	0.38	12.5	7	1	white	1
72	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	1
73	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
74	5.7	0.26	0.25	10.4	0.02	7.0	57.0	0.994	3.39	0.37	10.6	5	0	white	0
75	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
76	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
77	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	0
78	5.7	0.33	0.32	1.4	0.043	28.0	93.0	0.9897	3.31	0.5	12.3	6	0	white	1
79	5.7	0.385	0.04	12.6	0.034	22.0	115.0	0.9964	3.28	0.63	9.9	6	0	white	0
80	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	0
81	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	1
82	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
83	5.8	0.18	0.37	1.2	0.036	19.0	74.0	0.98853	3.09	0.49	12.7	7	1	white	1
84	5.8	0.19	0.24	1.3	0.044	38.0	128.0	0.99362	3.77	0.6	10.6	5	0	white	0
85	5.8	0.19	0.25	10.8	0.042	33.0	124.0	0.99646	3.22	0.41	9.2	6	0	white	0
86	5.8	0.2	0.24	1.4	0.033	65.0	169.0	0.99043	3.59	0.56	12.3	7	1	white	0
87	5.8	0.2	0.27	1.4	0.031	12.0	77.0	0.9905	3.25	0.36	10.9	7	1	white	0
88	5.8	0.2	0.3	1.5	0.031	21.0	57.0	0.99115	3.44	0.55	11.0	6	0	white	0
89	5.8	0.24	0.26	10.05	0.039	63.0	162.0	0.99375	3.33	0.5	11.2	6	0	white	0
90	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
91	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	0
92	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
93	5.8	0.27	0.22	12.7	0.058	42.0	206.0	0.9946	3.32	0.38	12.3	6	0	white	0
94	5.8	0.27	0.27	12.3	0.045	55.0	170.0	0.9972	3.28	0.42	9.3	6	0	white	0
95	5.8	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white	0
96	5.8	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	1
97	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
98	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0
99	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	0
100	5.8	0.39	0.47	7.5	0.027	12.0	88.0	0.9907	3.38	0.45	14.0	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	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	0	0.664607	0.335393
2	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	0	0.902349	0.0976514
3	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	0	0.75	0.25
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	0.900185	0.0998152
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	0	0.900185	0.0998152
6	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	0	0.664607	0.335393
7	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	1	0.493573	0.506427
8	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	0	0.664607	0.335393
9	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	1	0.493573	0.506427
10	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	0	0.664607	0.335393
11	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	0	0.75	0.25
12	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	1	0.493573	0.506427
13	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	1	0.493573	0.506427
14	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	0	0.902349	0.0976514
15	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	0	0.664607	0.335393
16	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	0.664607	0.335393
17	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	1	0.493573	0.506427
18	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	0	0.664607	0.335393
19	5.2	0.25	0.23	1.4	0.047	20.0	77.0	0.99001	3.32	0.62	11.4	5	0	white	0	0.75	0.25
20	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	1	0.493573	0.506427
21	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	0	0.75	0.25
22	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	0	0.75	0.25
23	5.2	0.49	0.26	2.3	0.09	23.0	74.0	0.9953	3.71	0.62	12.2	6	0	red	0	0.900185	0.0998152
24	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	0	0.75	0.25
25	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	0	0.664607	0.335393
26	5.3	0.24	0.33	1.3	0.033	25.0	97.0	0.9906	3.59	0.38	11.0	8	1	white	0	0.664607	0.335393
27	5.3	0.3	0.2	1.1	0.077	48.0	166.0	0.9944	3.3	0.54	8.7	4	0	white	0	0.75	0.25
28	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	1	0.493573	0.506427
29	5.3	0.57	0.01	1.7	0.054	5.0	27.0	0.9934	3.57	0.84	12.5	7	1	red	0	0.75	0.25
30	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	0	0.75	0.25
31	5.4	0.15	0.32	2.5	0.037	10.0	51.0	0.98878	3.04	0.58	12.6	6	0	white	1	0.493573	0.506427
32	5.4	0.17	0.27	2.7	0.049	28.0	104.0	0.99224	3.46	0.55	10.3	6	0	white	0	0.900185	0.0998152
33	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	0.664607	0.335393
34	5.4	0.23	0.36	1.5	0.03	74.0	121.0	0.98976	3.24	0.99	12.1	7	1	white	1	0.493573	0.506427
35	5.4	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	6	0	white	0	0.900185	0.0998152
36	5.4	0.27	0.22	4.6	0.022	29.0	107.0	0.98889	3.33	0.54	13.8	6	0	white	1	0.493573	0.506427
37	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	1	0.493573	0.506427
38	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	0	0.664607	0.335393
39	5.4	0.835	0.08	1.2	0.046	13.0	93.0	0.9924	3.57	0.85	13.0	7	1	red	0	0.664607	0.335393
40	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	0	0.664607	0.335393
41	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0	0.664607	0.335393
42	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	0	0.664607	0.335393
43	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	0.664607	0.335393
44	5.5	0.28	0.21	1.6	0.032	23.0	85.0	0.99027	3.42	0.42	12.5	5	0	white	1	0.493573	0.506427
45	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	1	0.493573	0.506427
46	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	1	0.493573	0.506427
47	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	0	0.900185	0.0998152
48	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	0	0.664607	0.335393
49	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	0	0.664607	0.335393
50	5.6	0.18	0.27	1.7	0.03	31.0	103.0	0.98892	3.35	0.37	12.9	6	0	white	1	0.493573	0.506427
51	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	0.664607	0.335393
52	5.6	0.2	0.66	10.2	0.043	78.0	175.0	0.9945	2.98	0.43	10.4	7	1	white	0	0.902349	0.0976514
53	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	0.664607	0.335393
54	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	0.902349	0.0976514
55	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	0.664607	0.335393
56	5.6	0.26	0.27	10.6	0.03	27.0	119.0	0.9947	3.4	0.34	10.7	7	1	white	0	0.902349	0.0976514
57	5.6	0.28	0.27	3.9	0.043	52.0	158.0	0.99202	3.35	0.44	10.7	7	1	white	0	0.664607	0.335393
58	5.6	0.29	0.05	0.8	0.038	11.0	30.0	0.9924	3.36	0.35	9.2	5	0	white	0	0.664607	0.335393
59	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	0.900185	0.0998152
60	5.6	0.33	0.28	1.2	0.031	33.0	97.0	0.99126	3.49	0.58	10.9	6	0	white	0	0.664607	0.335393
61	5.6	0.35	0.37	1.0	0.038	6.0	72.0	0.9902	3.37	0.34	11.4	5	0	white	1	0.493573	0.506427
62	5.6	0.39	0.24	4.7	0.034	27.0	77.0	0.9906	3.28	0.36	12.7	5	0	white	0	0.664607	0.335393
63	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	0.959358	0.0406417
64	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	0.664607	0.335393
65	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	0	0.959358	0.0406417
66	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	0.959358	0.0406417
67	5.6	0.66	0.0	2.5	0.066	7.0	15.0	0.99256	3.52	0.58	12.9	5	0	red	0	0.959358	0.0406417
68	5.7	0.1	0.27	1.3	0.047	21.0	100.0	0.9928	3.27	0.46	9.5	5	0	white	0	0.900185	0.0998152
69	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	0	0.902349	0.0976514
70	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	0	0.902349	0.0976514
71	5.7	0.22	0.28	1.3	0.027	26.0	101.0	0.98948	3.35	0.38	12.5	7	1	white	1	0.493573	0.506427
72	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	1	0.493573	0.506427
73	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	0.900185	0.0998152
74	5.7	0.26	0.25	10.4	0.02	7.0	57.0	0.994	3.39	0.37	10.6	5	0	white	0	0.902349	0.0976514
75	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	0.900185	0.0998152
76	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	0.959358	0.0406417
77	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	0	0.664607	0.335393
78	5.7	0.33	0.32	1.4	0.043	28.0	93.0	0.9897	3.31	0.5	12.3	6	0	white	1	0.493573	0.506427
79	5.7	0.385	0.04	12.6	0.034	22.0	115.0	0.9964	3.28	0.63	9.9	6	0	white	0	0.902349	0.0976514
80	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	0	0.712418	0.287582
81	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	1	0.493573	0.506427
82	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	0.664607	0.335393
83	5.8	0.18	0.37	1.2	0.036	19.0	74.0	0.98853	3.09	0.49	12.7	7	1	white	1	0.493573	0.506427
84	5.8	0.19	0.24	1.3	0.044	38.0	128.0	0.99362	3.77	0.6	10.6	5	0	white	0	0.712418	0.287582
85	5.8	0.19	0.25	10.8	0.042	33.0	124.0	0.99646	3.22	0.41	9.2	6	0	white	0	0.712418	0.287582
86	5.8	0.2	0.24	1.4	0.033	65.0	169.0	0.99043	3.59	0.56	12.3	7	1	white	0	0.664607	0.335393
87	5.8	0.2	0.27	1.4	0.031	12.0	77.0	0.9905	3.25	0.36	10.9	7	1	white	0	0.664607	0.335393
88	5.8	0.2	0.3	1.5	0.031	21.0	57.0	0.99115	3.44	0.55	11.0	6	0	white	0	0.664607	0.335393
89	5.8	0.24	0.26	10.05	0.039	63.0	162.0	0.99375	3.33	0.5	11.2	6	0	white	0	0.902349	0.0976514
90	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	0.664607	0.335393
91	5.8	0.26	0.24	9.2	0.044	55.0	152.0	0.9961	3.31	0.38	9.4	5	0	white	0	0.902349	0.0976514
92	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	0.902349	0.0976514
93	5.8	0.27	0.22	12.7	0.058	42.0	206.0	0.9946	3.32	0.38	12.3	6	0	white	0	0.959358	0.0406417
94	5.8	0.27	0.27	12.3	0.045	55.0	170.0	0.9972	3.28	0.42	9.3	6	0	white	0	0.902349	0.0976514
95	5.8	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white	0	0.664607	0.335393
96	5.8	0.28	0.3	3.9	0.026	36.0	105.0	0.98963	3.26	0.58	12.75	6	0	white	1	0.493573	0.506427
97	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	0.902349	0.0976514
98	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0	0.664607	0.335393
99	5.8	0.33	0.2	16.05	0.047	26.0	166.0	0.9976	3.09	0.46	8.9	5	0	white	0	0.959358	0.0406417
100	5.8	0.39	0.47	7.5	0.027	12.0	88.0	0.9907	3.38	0.45	14.0	6	0	white	0	0.664607	0.335393

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([[998,  40],
       [216,  48]])

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()

../_images/machine_learning_vertica_tree_decision_classifier.png

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="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.046625", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.046625", color="#000000", fontcolor="#000000"]\n1 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 0.993594", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 0.993594", color="#000000", fontcolor="#000000"]\n2 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.259375", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.259375", color="#000000", fontcolor="#000000"]\n3 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 0.990352", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 0.990352", color="#000000", fontcolor="#000000"]\n4 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n4 -> 9 [label="<= 0.197188", color="#000000", fontcolor="#000000"]\n4 -> 10 [label="> 0.197188", color="#000000", fontcolor="#000000"]\n5 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 11 [label="<= 5.4", color="#000000", fontcolor="#000000"]\n5 -> 12 [label="> 5.4", color="#000000", fontcolor="#000000"]\n6 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 13 [label="<= 6.9", color="#000000", fontcolor="#000000"]\n6 -> 14 [label="> 6.9", color="#000000", fontcolor="#000000"]\n7 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#efc5b5" color="#000000"><FONT color="#000000"><b>prediction: 1 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">prob(0): 0.49</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.51</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n8 [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">prob(0): 0.66</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.34</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n9 [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">prob(0): 0.71</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.29</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n10 [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">prob(0): 0.9</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.1</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n11 [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">prob(0): 0.75</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.25</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n12 [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">prob(0): 0.96</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.04</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n13 [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">prob(0): 0.9</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.1</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\n14 [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">prob(0): 0.82</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.18</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_features': 'auto',
 'max_leaf_nodes': 32,
 'max_depth': 3,
 'min_samples_leaf': 5,
 'min_info_gain': 0.0,
 'nbins': 32}

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

To SQL

You can get the SQL code by:

model.to_sql()
Out[8]: '(CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "density" < 0.990352 THEN 1 ELSE 0 END) ELSE (CASE WHEN "volatile_acidity" < 0.197188 THEN 0 ELSE 0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "fixed_acidity" < 5.4 THEN 0 ELSE 0 END) ELSE (CASE WHEN "fixed_acidity" < 6.9 THEN 0 ELSE 0 END) END) END)'

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[10]: array([1])

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_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32) → None¶: Must be overridden in the child class

Methods

`__init__`([name, overwrite_model, ...])	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_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