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verticapy.machine_learning.vertica.ensemble.RandomForestClassifier

class verticapy.machine_learning.vertica.ensemble.RandomForestClassifier(name: str = None, overwrite_model: bool = False, n_estimators: int = 10, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32)

Creates a RandomForestClassifier object using the Vertica RF_CLASSIFIER function. It is an ensemble learning method for classification that operates by constructing a multitude of decision trees during training and predicting the class by using a majority voting strategy among the individual trees.

The classifier aggregates the predictions of multiple decision trees to determine the final output class, thus improving the overall prediction accuracy and robustness compared to a single decision tree.

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.

n_estimators: int, optional

The number of trees in the forest, an integer between 1 and 1000, inclusive.

max_features: int | str, optional

The number of randomly chosen features from which to pick the best feature to split 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 integerv between ``1 and 1e9, inclusive.

sample: float, optional

The portion of the input data set that is randomly selected for training each tree, a float between 0.0 and 1.0, inclusive.

max_depth: int, optional

aximum depth of each tree, an integer between 1 and 100, inclusive.

min_samples_leaf: int, optional

The minimum number of samples each branch must have after splitting a node, an integer between 1 and 1e6, inclusive. A split that results in remaining samples less than this value 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

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.

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 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.

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 MDI (Mean Decreased Impurity). It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

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 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 RandomForestClassifier model:

from verticapy.machine_learning.vertica import RandomForestClassifier

Then we can create the model:

model = RandomForestClassifier(
    max_features = "auto",
    max_leaf_nodes = 32,
    sample = 0.5,
    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_c0dd8c5455a311ef880f0242ac120002_"', '"public"."_verticapy_tmp_view_v_demo_c0eb0ffa55a311ef880f0242ac120002_"', 'good', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', ntree=10, mtry=3, sampling_size=0.5, 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    | 10  
rejected_row_count|  0  
accepted_row_count|5198 

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	0.7539107774308096
prc_auc	0.45657884241205604
accuracy	0.8098537336412626
log_loss	0.192189874929422
precision	0.8181818181818182
recall	0.06896551724137931
f1_score	0.127208480565371
mcc	0.20219614039002926
informedness	0.06511195269417303
markedness	0.6278920765999858
csi	0.06792452830188679

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	0.7539107774308096
prc_auc	0.45657884241205604
accuracy	0.7205542725173211
log_loss	0.192189874929422
precision	0.37376237623762376
recall	0.578544061302682
f1_score	0.45413533834586467
mcc	0.28980781799041244
informedness	0.3348061036918919
markedness	0.2508573483046628
csi	0.29377431906614787

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]: 0.8098537336412626

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	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white	0
2	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	0
3	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	0
4	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white	0
5	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	0
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	0
7	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	0
8	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	0
9	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	1
10	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	0
11	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	0
12	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	0
13	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	0
14	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
15	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	0
16	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
17	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	0
18	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	0
19	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
20	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	0
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	0
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	0
23	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7	1	white	0
24	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
25	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red	0
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	0
27	5.2	0.405	0.15	1.45	0.038	10.0	44.0	0.99125	3.52	0.4	11.6	4	0	white	0
28	5.3	0.275	0.24	7.4	0.038	28.0	114.0	0.99313	3.38	0.51	11.0	6	0	white	0
29	5.3	0.395	0.07	1.3	0.035	26.0	102.0	0.992	3.5	0.35	10.6	6	0	white	0
30	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
31	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	0
32	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0
33	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0
34	5.4	0.415	0.19	1.6	0.039	27.0	88.0	0.99265	3.54	0.41	10.0	7	1	white	0
35	5.4	0.45	0.27	6.4	0.033	20.0	102.0	0.98944	3.22	0.27	13.4	8	1	white	1
36	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white	1
37	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0
38	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
39	5.5	0.15	0.32	14.0	0.031	16.0	99.0	0.99437	3.26	0.38	11.5	8	1	white	0
40	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	0
41	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	0
42	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	0
43	5.6	0.13	0.27	4.8	0.028	22.0	104.0	0.9948	3.34	0.45	9.2	6	0	white	0
44	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
45	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	0
46	5.6	0.2	0.36	2.5	0.048	16.0	125.0	0.99282	3.49	0.49	10.0	6	0	white	0
47	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	0
48	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
49	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	0
50	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
51	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white	0
52	5.6	0.32	0.32	8.3	0.043	32.0	105.0	0.99266	3.24	0.47	11.2	6	0	white	0
53	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white	0
54	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
55	5.6	0.54	0.04	1.7	0.049	5.0	13.0	0.9942	3.72	0.58	11.4	5	0	red	0
56	5.7	0.135	0.3	4.6	0.042	19.0	101.0	0.9946	3.31	0.42	9.3	6	0	white	0
57	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
58	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	0
59	5.7	0.21	0.32	1.6	0.03	33.0	122.0	0.99044	3.33	0.52	11.9	6	0	white	0
60	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
61	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	0
62	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white	0
63	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white	0
64	5.7	0.25	0.21	1.5	0.044	21.0	108.0	0.99142	3.3	0.59	11.0	6	0	white	0
65	5.7	0.25	0.22	9.8	0.049	50.0	125.0	0.99571	3.2	0.45	10.1	6	0	white	0
66	5.7	0.25	0.32	12.2	0.041	43.0	127.0	0.99524	3.23	0.53	10.4	7	1	white	0
67	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white	0
68	5.7	0.28	0.35	1.2	0.052	39.0	141.0	0.99108	3.44	0.69	11.3	6	0	white	0
69	5.7	0.29	0.16	7.9	0.044	48.0	197.0	0.99512	3.21	0.36	9.4	5	0	white	0
70	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	0
71	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	0
72	5.7	0.33	0.15	1.9	0.05	20.0	93.0	0.9934	3.38	0.62	9.9	5	0	white	0
73	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	0
74	5.7	0.36	0.21	6.7	0.038	51.0	166.0	0.9941	3.29	0.63	10.0	6	0	white	0
75	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
76	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	1
77	5.7	0.6	0.0	1.4	0.063	11.0	18.0	0.99191	3.45	0.56	12.2	6	0	red	0
78	5.8	0.13	0.22	12.7	0.058	24.0	183.0	0.9956	3.32	0.42	11.7	6	0	white	0
79	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	0
80	5.8	0.15	0.31	5.9	0.036	7.0	73.0	0.99152	3.2	0.43	11.9	6	0	white	0
81	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	0
82	5.8	0.19	0.33	4.2	0.038	49.0	133.0	0.99107	3.16	0.42	11.3	7	1	white	0
83	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
84	5.8	0.21	0.32	1.6	0.045	38.0	95.0	0.98946	3.23	0.94	12.4	8	1	white	0
85	5.8	0.22	0.25	1.5	0.024	21.0	109.0	0.99234	3.37	0.58	10.4	6	0	white	0
86	5.8	0.23	0.31	3.5	0.044	35.0	158.0	0.98998	3.19	0.37	12.1	7	1	white	0
87	5.8	0.24	0.28	1.4	0.038	40.0	76.0	0.98711	3.1	0.29	13.9	7	1	white	0
88	5.8	0.24	0.44	3.5	0.029	5.0	109.0	0.9913	3.53	0.43	11.7	3	0	white	0
89	5.8	0.25	0.26	13.1	0.051	44.0	148.0	0.9972	3.29	0.38	9.3	5	0	white	0
90	5.8	0.27	0.2	14.95	0.044	22.0	179.0	0.9962	3.37	0.37	10.2	5	0	white	0
91	5.8	0.27	0.26	3.5	0.071	26.0	69.0	0.98994	3.1	0.38	11.5	6	0	white	0
92	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	0
93	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
94	5.8	0.29	0.15	1.1	0.029	12.0	83.0	0.9898	3.3	0.4	11.4	6	0	white	0
95	5.8	0.29	0.21	2.6	0.025	12.0	120.0	0.9894	3.39	0.79	14.0	7	1	white	0
96	5.8	0.3	0.33	3.5	0.033	25.0	116.0	0.99057	3.2	0.44	11.7	6	0	white	0
97	5.8	0.315	0.27	1.55	0.026	15.0	70.0	0.98994	3.37	0.4	11.9	8	1	white	0
98	5.8	0.32	0.2	2.6	0.027	17.0	123.0	0.98936	3.36	0.78	13.9	7	1	white	0
99	5.8	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white	0
100	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

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	4.7	0.67	0.09	1.0	0.02	5.0	9.0	0.98722	3.3	0.34	13.6	5	0	white	0	0.552439	0.447561
2	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	0	0.544863	0.455137
3	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	0	0.851713	0.148287
4	4.8	0.26	0.23	10.6	0.034	23.0	111.0	0.99274	3.46	0.28	11.5	7	1	white	0	0.872944	0.127056
5	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	0	0.885677	0.114323
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	0	0.664936	0.335064
7	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	0	0.643427	0.356573
8	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	0	0.507859	0.492141
9	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	1	0.482885	0.517115
10	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	0	0.595448	0.404552
11	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	0	0.552439	0.447561
12	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	0	0.911622	0.0883784
13	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	0	0.593929	0.406071
14	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	0.694709	0.305291
15	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	0	0.552239	0.447761
16	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	0.765483	0.234517
17	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	0	0.826785	0.173215
18	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	0	0.885677	0.114323
19	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	0.894292	0.105708
20	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	0	0.5556	0.4444
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	0	0.798428	0.201572
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	0	0.564933	0.435067
23	5.2	0.31	0.36	5.1	0.031	46.0	145.0	0.9897	3.14	0.31	12.4	7	1	white	0	0.507859	0.492141
24	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	0.926725	0.073275
25	5.2	0.34	0.0	1.8	0.05	27.0	63.0	0.9916	3.68	0.79	14.0	6	0	red	0	0.734934	0.265066
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	0	0.928021	0.0719794
27	5.2	0.405	0.15	1.45	0.038	10.0	44.0	0.99125	3.52	0.4	11.6	4	0	white	0	0.643427	0.356573
28	5.3	0.275	0.24	7.4	0.038	28.0	114.0	0.99313	3.38	0.51	11.0	6	0	white	0	0.872944	0.127056
29	5.3	0.395	0.07	1.3	0.035	26.0	102.0	0.992	3.5	0.35	10.6	6	0	white	0	0.885677	0.114323
30	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	0.775615	0.224385
31	5.4	0.205	0.16	12.55	0.051	31.0	115.0	0.99564	3.4	0.38	10.8	6	0	white	0	0.891667	0.108333
32	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0	0.618118	0.381882
33	5.4	0.29	0.38	1.2	0.029	31.0	132.0	0.98895	3.28	0.36	12.4	6	0	white	0	0.618118	0.381882
34	5.4	0.415	0.19	1.6	0.039	27.0	88.0	0.99265	3.54	0.41	10.0	7	1	white	0	0.885677	0.114323
35	5.4	0.45	0.27	6.4	0.033	20.0	102.0	0.98944	3.22	0.27	13.4	8	1	white	1	0.492431	0.507569
36	5.4	0.5	0.13	5.0	0.028	12.0	107.0	0.99079	3.48	0.88	13.5	7	1	white	1	0.496786	0.503214
37	5.4	0.53	0.16	2.7	0.036	34.0	128.0	0.98856	3.2	0.53	13.2	8	1	white	0	0.533828	0.466172
38	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	0.899884	0.100116
39	5.5	0.15	0.32	14.0	0.031	16.0	99.0	0.99437	3.26	0.38	11.5	8	1	white	0	0.795759	0.204241
40	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	0	0.789626	0.210374
41	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	0	0.817144	0.182856
42	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	0	0.5785	0.4215
43	5.6	0.13	0.27	4.8	0.028	22.0	104.0	0.9948	3.34	0.45	9.2	6	0	white	0	0.757594	0.242406
44	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	0.775615	0.224385
45	5.6	0.19	0.26	1.4	0.03	12.0	76.0	0.9905	3.25	0.37	10.9	7	1	white	0	0.5556	0.4444
46	5.6	0.2	0.36	2.5	0.048	16.0	125.0	0.99282	3.49	0.49	10.0	6	0	white	0	0.775615	0.224385
47	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	0	0.552239	0.447761
48	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	0.577213	0.422787
49	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	0	0.826785	0.173215
50	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	0.826785	0.173215
51	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white	0	0.902407	0.0975926
52	5.6	0.32	0.32	8.3	0.043	32.0	105.0	0.99266	3.24	0.47	11.2	6	0	white	0	0.820557	0.179443
53	5.6	0.34	0.1	1.3	0.031	20.0	68.0	0.9906	3.36	0.51	11.2	7	1	white	0	0.643427	0.356573
54	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	0.900827	0.0991726
55	5.6	0.54	0.04	1.7	0.049	5.0	13.0	0.9942	3.72	0.58	11.4	5	0	red	0	0.902407	0.0975926
56	5.7	0.135	0.3	4.6	0.042	19.0	101.0	0.9946	3.31	0.42	9.3	6	0	white	0	0.792346	0.207654
57	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	0.54595	0.45405
58	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	0	0.593929	0.406071
59	5.7	0.21	0.32	1.6	0.03	33.0	122.0	0.99044	3.33	0.52	11.9	6	0	white	0	0.5556	0.4444
60	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	0.887774	0.112226
61	5.7	0.22	0.22	16.65	0.044	39.0	110.0	0.99855	3.24	0.48	9.0	6	0	white	0	0.871628	0.128372
62	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white	0	0.507859	0.492141
63	5.7	0.245	0.33	1.1	0.049	28.0	150.0	0.9927	3.13	0.42	9.3	5	0	white	0	0.817144	0.182856
64	5.7	0.25	0.21	1.5	0.044	21.0	108.0	0.99142	3.3	0.59	11.0	6	0	white	0	0.643427	0.356573
65	5.7	0.25	0.22	9.8	0.049	50.0	125.0	0.99571	3.2	0.45	10.1	6	0	white	0	0.911875	0.0881246
66	5.7	0.25	0.32	12.2	0.041	43.0	127.0	0.99524	3.23	0.53	10.4	7	1	white	0	0.87759	0.12241
67	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white	0	0.92186	0.07814
68	5.7	0.28	0.35	1.2	0.052	39.0	141.0	0.99108	3.44	0.69	11.3	6	0	white	0	0.680365	0.319635
69	5.7	0.29	0.16	7.9	0.044	48.0	197.0	0.99512	3.21	0.36	9.4	5	0	white	0	0.90582	0.0941796
70	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	0	0.568295	0.431705
71	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	0	0.530521	0.469479
72	5.7	0.33	0.15	1.9	0.05	20.0	93.0	0.9934	3.38	0.62	9.9	5	0	white	0	0.897414	0.102586
73	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	0	0.561785	0.438215
74	5.7	0.36	0.21	6.7	0.038	51.0	166.0	0.9941	3.29	0.63	10.0	6	0	white	0	0.886262	0.113738
75	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	0.928021	0.0719794
76	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	1	0.494964	0.505036
77	5.7	0.6	0.0	1.4	0.063	11.0	18.0	0.99191	3.45	0.56	12.2	6	0	red	0	0.72244	0.27756
78	5.8	0.13	0.22	12.7	0.058	24.0	183.0	0.9956	3.32	0.42	11.7	6	0	white	0	0.891667	0.108333
79	5.8	0.13	0.26	5.1	0.039	19.0	103.0	0.99478	3.36	0.47	9.3	6	0	white	0	0.792346	0.207654
80	5.8	0.15	0.31	5.9	0.036	7.0	73.0	0.99152	3.2	0.43	11.9	6	0	white	0	0.54595	0.45405
81	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	0	0.577213	0.422787
82	5.8	0.19	0.33	4.2	0.038	49.0	133.0	0.99107	3.16	0.42	11.3	7	1	white	0	0.54595	0.45405
83	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	0.593929	0.406071
84	5.8	0.21	0.32	1.6	0.045	38.0	95.0	0.98946	3.23	0.94	12.4	8	1	white	0	0.577213	0.422787
85	5.8	0.22	0.25	1.5	0.024	21.0	109.0	0.99234	3.37	0.58	10.4	6	0	white	0	0.776744	0.223256
86	5.8	0.23	0.31	3.5	0.044	35.0	158.0	0.98998	3.19	0.37	12.1	7	1	white	0	0.507859	0.492141
87	5.8	0.24	0.28	1.4	0.038	40.0	76.0	0.98711	3.1	0.29	13.9	7	1	white	0	0.577213	0.422787
88	5.8	0.24	0.44	3.5	0.029	5.0	109.0	0.9913	3.53	0.43	11.7	3	0	white	0	0.542426	0.457574
89	5.8	0.25	0.26	13.1	0.051	44.0	148.0	0.9972	3.29	0.38	9.3	5	0	white	0	0.885035	0.114965
90	5.8	0.27	0.2	14.95	0.044	22.0	179.0	0.9962	3.37	0.37	10.2	5	0	white	0	0.928021	0.0719794
91	5.8	0.27	0.26	3.5	0.071	26.0	69.0	0.98994	3.1	0.38	11.5	6	0	white	0	0.589313	0.410687
92	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	0	0.680365	0.319635
93	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	0.482885	0.517115
94	5.8	0.29	0.15	1.1	0.029	12.0	83.0	0.9898	3.3	0.4	11.4	6	0	white	0	0.564933	0.435067
95	5.8	0.29	0.21	2.6	0.025	12.0	120.0	0.9894	3.39	0.79	14.0	7	1	white	0	0.564933	0.435067
96	5.8	0.3	0.33	3.5	0.033	25.0	116.0	0.99057	3.2	0.44	11.7	6	0	white	0	0.54595	0.45405
97	5.8	0.315	0.27	1.55	0.026	15.0	70.0	0.98994	3.37	0.4	11.9	8	1	white	0	0.53681	0.46319
98	5.8	0.32	0.2	2.6	0.027	17.0	123.0	0.98936	3.36	0.78	13.9	7	1	white	0	0.564933	0.435067
99	5.8	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white	0	0.530521	0.469479
100	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	0.928021	0.0719794

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([[1035,    3],
       [ 248,   13]])

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="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.049437", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.049437", color="#000000", fontcolor="#000000"]\n1 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 0.991991", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 0.991991", 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="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 2.6375", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 2.6375", color="#000000", fontcolor="#000000"]\n4 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n4 -> 9 [label="<= 0.2075", color="#000000", fontcolor="#000000"]\n4 -> 10 [label="> 0.2075", color="#000000", fontcolor="#000000"]\n5 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 11 [label="<= 0.995232", color="#000000", fontcolor="#000000"]\n5 -> 12 [label="> 0.995232", color="#000000", fontcolor="#000000"]\n6 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 13 [label="<= 9.09375", color="#000000", fontcolor="#000000"]\n6 -> 14 [label="> 9.09375", color="#000000", fontcolor="#000000"]\n7 [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): 0.61</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.39</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="#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): 0.43</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.57</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): 0.98</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.02</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): 0.82</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"]\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): 0.91</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.09</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): 0.98</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.02</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): 0.86</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.14</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): 0.76</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.24</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]: 
{'n_estimators': 10,
 'max_features': 'auto',
 'max_leaf_nodes': 32,
 'sample': 0.5,
 '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 ((CASE WHEN "chlorides" < 0.049437 THEN (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "citric_acid" < 0.2075 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "density" < 0.995232 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 9.09375 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "density" < 0.990371 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "chlorides" < 0.049437 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "density" < 0.996852 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995232 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "chlorides" < 0.030719 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "volatile_acidity" < 0.314375 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "density" < 0.995232 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "volatile_acidity" < 0.54875 THEN (CASE WHEN "residual_sugar" < 6.7125 THEN (CASE WHEN "density" < 0.991991 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "chlorides" < 0.030719 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.991991 THEN 0.0 ELSE (CASE WHEN "volatile_acidity" < 0.595625 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "chlorides" < 0.030719 THEN (CASE WHEN "citric_acid" < 0.363125 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.466875 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.31125 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "fixed_acidity" < 7.959375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 7.203125 THEN 1.0 ELSE 0.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE (CASE WHEN "density" < 0.995232 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "volatile_acidity" < 0.408125 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995232 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "fixed_acidity" < 7.58125 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "chlorides" < 0.049437 THEN (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "volatile_acidity" < 0.126875 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.993611 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "fixed_acidity" < 10.228125 THEN (CASE WHEN "residual_sugar" < 10.7875 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.36125 THEN 1.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "fixed_acidity" < 7.58125 THEN (CASE WHEN "citric_acid" < 0.466875 THEN 0.0 ELSE 0.0 END) ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 9.85 THEN (CASE WHEN "volatile_acidity" < 0.220625 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.455 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "chlorides" < 0.030719 THEN (CASE WHEN "citric_acid" < 0.363125 THEN (CASE WHEN "fixed_acidity" < 8.3375 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.991991 THEN (CASE WHEN "density" < 0.990371 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995232 THEN 0.0 ELSE 0.0 END) END) END)) / 10 > 0.5 THEN 1 ELSE 0 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([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, n_estimators: int = 10, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, sample: float = 0.632, max_depth: int = 5, 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