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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	3.9	0.225	0.4	4.2	0.03	29.0	118.0	0.989	3.57	0.36	12.8	8	1	white
2	4.7	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.5	5	0	white
3	4.7	0.455	0.18	1.9	0.036	33.0	106.0	0.98746	3.21	0.83	14.0	7	1	white
4	4.7	0.785	0.0	3.4	0.036	23.0	134.0	0.98981	3.53	0.92	13.8	6	0	white
5	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
6	4.9	0.345	0.34	1.0	0.068	32.0	143.0	0.99138	3.24	0.4	10.1	5	0	white
7	4.9	0.42	0.0	2.1	0.048	16.0	42.0	0.99154	3.71	0.74	14.0	7	1	red
8	5.0	0.27	0.4	1.2	0.076	42.0	124.0	0.99204	3.32	0.47	10.1	6	0	white
9	5.0	0.31	0.0	6.4	0.046	43.0	166.0	0.994	3.3	0.63	9.9	6	0	white
10	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red
11	5.0	0.44	0.04	18.6	0.039	38.0	128.0	0.9985	3.37	0.57	10.2	6	0	white
12	5.1	0.11	0.32	1.6	0.028	12.0	90.0	0.99008	3.57	0.52	12.2	6	0	white
13	5.1	0.14	0.25	0.7	0.039	15.0	89.0	0.9919	3.22	0.43	9.2	6	0	white
14	5.1	0.165	0.22	5.7	0.047	42.0	146.0	0.9934	3.18	0.55	9.9	6	0	white
15	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
16	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
17	5.1	0.33	0.22	1.6	0.027	18.0	89.0	0.9893	3.51	0.38	12.5	7	1	white
18	5.1	0.39	0.21	1.7	0.027	15.0	72.0	0.9894	3.5	0.45	12.5	6	0	white
19	5.2	0.2	0.27	3.2	0.047	16.0	93.0	0.99235	3.44	0.53	10.1	7	1	white
20	5.2	0.21	0.31	1.7	0.048	17.0	61.0	0.98953	3.24	0.37	12.0	7	1	white
21	5.2	0.22	0.46	6.2	0.066	41.0	187.0	0.99362	3.19	0.42	9.73333333333333	5	0	white
22	5.2	0.31	0.2	2.4	0.027	27.0	117.0	0.98886	3.56	0.45	13.0	7	1	white
23	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red
24	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white
25	5.2	0.36	0.02	1.6	0.031	24.0	104.0	0.9896	3.44	0.35	12.2	6	0	white
26	5.2	0.365	0.08	13.5	0.041	37.0	142.0	0.997	3.46	0.39	9.9	6	0	white
27	5.2	0.48	0.04	1.6	0.054	19.0	106.0	0.9927	3.54	0.62	12.2	7	1	red
28	5.2	0.5	0.18	2.0	0.036	23.0	129.0	0.98949	3.36	0.77	13.4	7	1	white
29	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
30	5.3	0.16	0.39	1.0	0.028	40.0	101.0	0.99156	3.57	0.59	10.6	6	0	white
31	5.3	0.165	0.24	1.1	0.051	25.0	105.0	0.9925	3.32	0.47	9.1	5	0	white
32	5.3	0.23	0.56	0.9	0.041	46.0	141.0	0.99119	3.16	0.62	9.7	5	0	white
33	5.3	0.3	0.3	1.2	0.029	25.0	93.0	0.98742	3.31	0.4	13.6	7	1	white
34	5.3	0.33	0.3	1.2	0.048	25.0	119.0	0.99045	3.32	0.62	11.3	6	0	white
35	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
36	5.3	0.36	0.27	6.3	0.028	40.0	132.0	0.99186	3.37	0.4	11.6	6	0	white
37	5.3	0.4	0.25	3.9	0.031	45.0	130.0	0.99072	3.31	0.58	11.75	7	1	white
38	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.6	7	1	red
39	5.3	0.47	0.11	2.2	0.048	16.0	89.0	0.99182	3.54	0.88	13.5666666666667	7	1	red
40	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red
41	5.4	0.22	0.29	1.2	0.045	69.0	152.0	0.99178	3.76	0.63	11.0	7	1	white
42	5.4	0.595	0.1	2.8	0.042	26.0	80.0	0.9932	3.36	0.38	9.3	5	0	white
43	5.4	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red
44	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
45	5.5	0.12	0.33	1.0	0.038	23.0	131.0	0.99164	3.25	0.45	9.8	5	0	white
46	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
47	5.5	0.14	0.27	4.6	0.029	22.0	104.0	0.9949	3.34	0.44	9.0	5	0	white
48	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.65	6	0	white
49	5.5	0.16	0.31	1.2	0.026	31.0	68.0	0.9898	3.33	0.44	11.6333333333333	6	0	white
50	5.5	0.18	0.22	5.5	0.037	10.0	86.0	0.99156	3.46	0.44	12.2	5	0	white
51	5.5	0.24	0.45	1.7	0.046	22.0	113.0	0.99224	3.22	0.48	10.0	5	0	white
52	5.5	0.29	0.3	1.1	0.022	20.0	110.0	0.98869	3.34	0.38	12.8	7	1	white
53	5.5	0.31	0.29	3.0	0.027	16.0	102.0	0.99067	3.23	0.56	11.2	6	0	white
54	5.5	0.32	0.45	4.9	0.028	25.0	191.0	0.9922	3.51	0.49	11.5	7	1	white
55	5.5	0.35	0.35	1.1	0.045	14.0	167.0	0.992	3.34	0.68	9.9	6	0	white
56	5.5	0.375	0.38	1.7	0.036	17.0	98.0	0.99142	3.29	0.39	10.5	6	0	white
57	5.6	0.15	0.26	5.55	0.051	51.0	139.0	0.99336	3.47	0.5	11.0	6	0	white
58	5.6	0.15	0.31	5.3	0.038	8.0	79.0	0.9923	3.3	0.39	10.5	6	0	white
59	5.6	0.16	0.27	1.4	0.044	53.0	168.0	0.9918	3.28	0.37	10.1	6	0	white
60	5.6	0.175	0.29	0.8	0.043	20.0	67.0	0.99112	3.28	0.48	9.9	6	0	white
61	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
62	5.6	0.185	0.19	7.1	0.048	36.0	110.0	0.99438	3.26	0.41	9.5	6	0	white
63	5.6	0.22	0.32	1.2	0.024	29.0	97.0	0.98823	3.2	0.46	13.05	7	1	white
64	5.6	0.26	0.18	1.4	0.034	18.0	135.0	0.99174	3.32	0.35	10.2	6	0	white
65	5.6	0.26	0.26	5.7	0.031	12.0	80.0	0.9923	3.25	0.38	10.8	5	0	white
66	5.6	0.26	0.5	11.4	0.029	25.0	93.0	0.99428	3.23	0.49	10.5	6	0	white
67	5.6	0.28	0.28	4.2	0.044	52.0	158.0	0.992	3.35	0.44	10.7	7	1	white
68	5.6	0.3	0.1	6.4	0.043	34.0	142.0	0.99382	3.14	0.48	9.8	5	0	white
69	5.6	0.35	0.14	5.0	0.046	48.0	198.0	0.9937	3.3	0.71	10.3	5	0	white
70	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
71	5.6	0.49	0.13	4.5	0.039	17.0	116.0	0.9907	3.42	0.9	13.7	7	1	white
72	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
73	5.6	0.66	0.0	2.2	0.087	3.0	11.0	0.99378	3.71	0.63	12.8	7	1	red
74	5.7	0.15	0.47	11.4	0.035	49.0	128.0	0.99456	3.03	0.34	10.5	8	1	white
75	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white
76	5.7	0.18	0.36	1.2	0.046	9.0	71.0	0.99199	3.7	0.68	10.9	7	1	white
77	5.7	0.2	0.3	2.5	0.046	38.0	125.0	0.99276	3.34	0.5	9.9	6	0	white
78	5.7	0.21	0.32	0.9	0.038	38.0	121.0	0.99074	3.24	0.46	10.6	6	0	white
79	5.7	0.21	0.37	4.5	0.04	58.0	140.0	0.99332	3.29	0.62	10.6	6	0	white
80	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
81	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
82	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
83	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
84	5.7	0.22	0.2	16.0	0.044	41.0	113.0	0.99862	3.22	0.46	8.9	6	0	white
85	5.7	0.22	0.29	3.5	0.04	27.0	146.0	0.98999	3.17	0.36	12.1	6	0	white
86	5.7	0.23	0.28	9.65	0.025	26.0	121.0	0.9925	3.28	0.38	11.3	6	0	white
87	5.7	0.25	0.26	12.5	0.049	52.5	106.0	0.99691	3.08	0.45	9.4	6	0	white
88	5.7	0.25	0.26	12.5	0.049	52.5	120.0	0.99691	3.08	0.45	9.4	6	0	white
89	5.7	0.25	0.27	11.5	0.04	24.0	120.0	0.99411	3.33	0.31	10.8	6	0	white
90	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
91	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
92	5.7	0.26	0.24	17.8	0.059	23.0	124.0	0.99773	3.3	0.5	10.1	5	0	white
93	5.7	0.27	0.32	1.2	0.046	20.0	155.0	0.9934	3.8	0.41	10.2	6	0	white
94	5.7	0.28	0.24	17.5	0.044	60.0	167.0	0.9989	3.31	0.44	9.4	5	0	white
95	5.7	0.32	0.18	1.4	0.029	26.0	104.0	0.9906	3.44	0.37	11.0	6	0	white
96	5.7	0.32	0.38	4.75	0.033	23.0	94.0	0.991	3.42	0.42	11.8	7	1	white
97	5.7	0.36	0.34	4.2	0.026	21.0	77.0	0.9907	3.41	0.45	11.9	6	0	white
98	5.8	0.14	0.15	6.1	0.042	27.0	123.0	0.99362	3.06	0.6	9.9	6	0	white
99	5.8	0.15	0.32	1.2	0.037	14.0	119.0	0.99137	3.19	0.5	10.2	6	0	white
100	5.8	0.17	0.34	1.8	0.045	96.0	170.0	0.99035	3.38	0.9	11.8	8	1	white

Rows: 1-100 | Columns: 14

Note

VerticaPy offers a wide range of sample datasets that are ideal for training and testing purposes. You can explore the full list of available datasets in the Datasets, which provides detailed information on each dataset and how to use them effectively. These datasets are invaluable resources for honing your data analysis and machine learning skills within the VerticaPy environment.

You can easily divide your dataset into training and testing subsets using the vDataFrame.train_test_split() method. This is a crucial step when preparing your data for machine learning, as it allows you to evaluate the performance of your models accurately.

data = vpd.load_winequality()
train, test = data.train_test_split(test_size = 0.2)

Warning

In this case, VerticaPy utilizes seeded randomization to guarantee the reproducibility of your data split. However, please be aware that this approach may lead to reduced performance. For a more efficient data split, you can use the vDataFrame.to_db() method to save your results into tables or temporary tables. This will help enhance the overall performance of the process.

Balancing the Dataset

In VerticaPy, balancing a dataset to address class imbalances is made straightforward through the balance() function within the preprocessing module. This function enables users to rectify skewed class distributions efficiently. By specifying the target variable and setting parameters like the method for balancing, users can effortlessly achieve a more equitable representation of classes in their dataset. Whether opting for over-sampling, under-sampling, or a combination of both, VerticaPy’s balance() function streamlines the process, empowering users to enhance the performance and fairness of their machine learning models trained on imbalanced data.

To balance the dataset, use the following syntax.

from verticapy.machine_learning.vertica.preprocessing import balance

balanced_train = balance(
    name = "my_schema.train_balanced",
    input_relation = train,
    y = "good",
    method = "hybrid",
)

Note

With this code, a table named train_balanced is created in the my_schema schema. It can then be used to train the model. In the rest of the example, we will work with the full dataset.

Hint

Balancing the dataset is a crucial step in improving the accuracy of machine learning models, particularly when faced with imbalanced class distributions. By addressing disparities in the number of instances across different classes, the model becomes more adept at learning patterns from all classes rather than being biased towards the majority class. This, in turn, enhances the model’s ability to make accurate predictions for under-represented classes. The balanced dataset ensures that the model is not dominated by the majority class and, as a result, leads to more robust and unbiased model performance. Therefore, by employing techniques such as over-sampling, under-sampling, or a combination of both during dataset preparation, practitioners can significantly contribute to achieving higher accuracy and better generalization of their machine learning models.

Model Initialization

First we import the 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_mldb_002d6e0c979811efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_005d85c4979811efa8720242ac120002_"', '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|5201 

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	0.7861225932074248
prc_auc	0.46152139539418646
accuracy	0.7970679012345679
log_loss	0.185798106186099
precision	0.0
recall	0.0
f1_score	0.0
mcc	0.0
informedness	0.0
markedness	-0.20293209876543206
csi	0.0

Rows: 1-11 | Columns: 2

Important

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

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

model.report(cutoff = 0.2)

	value
auc	0.7861225932074248
prc_auc	0.46152139539418646
accuracy	0.7222222222222222
log_loss	0.185798106186099
precision	0.39832285115303984
recall	0.7224334600760456
f1_score	0.5135135135135135
mcc	0.37076455618237414
informedness	0.4446019015087659
markedness	0.30918976201995063
csi	0.34545454545454546

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]: 0.7970679012345679

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	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white	0
2	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	0
3	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	0
4	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	0
5	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	0
6	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	0
7	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6	0	white	0
8	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	0
9	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white	0
10	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white	0
11	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
12	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
13	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	0
14	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	0
15	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
16	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
17	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
18	5.7	0.14	0.3	5.4	0.045	26.0	105.0	0.99469	3.32	0.45	9.3	5	0	white	0
19	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
20	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
21	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
22	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0
23	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
24	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
25	5.8	0.25	0.28	11.1	0.056	45.0	175.0	0.99755	3.42	0.43	9.5	5	0	white	0
26	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
27	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
28	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
29	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
30	5.8	0.34	0.16	7.0	0.037	26.0	116.0	0.9949	3.46	0.45	10.0	7	1	white	0
31	5.8	0.36	0.26	3.3	0.038	40.0	153.0	0.9911	3.34	0.55	11.3	6	0	white	0
32	5.8	0.36	0.5	1.0	0.127	63.0	178.0	0.99212	3.1	0.45	9.7	5	0	white	0
33	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	0
34	5.9	0.17	0.28	0.7	0.027	5.0	28.0	0.98985	3.13	0.32	10.6	5	0	white	0
35	5.9	0.17	0.29	3.1	0.03	32.0	123.0	0.98913	3.41	0.33	13.7	7	1	white	0
36	5.9	0.2	0.23	1.5	0.037	38.0	93.0	0.99021	3.36	0.49	12.0	6	0	white	0
37	5.9	0.25	0.19	12.4	0.047	50.0	162.0	0.9973	3.35	0.38	9.5	5	0	white	0
38	5.9	0.27	0.27	9.0	0.051	43.0	136.0	0.9941	3.25	0.53	10.7	6	0	white	0
39	5.9	0.28	0.34	3.6	0.04	50.0	194.0	0.9912	3.31	0.52	11.6	6	0	white	0
40	5.9	0.29	0.25	13.4	0.067	72.0	160.0	0.99721	3.33	0.54	10.3	6	0	red	0
41	5.9	0.35	0.47	2.2	0.11	14.0	138.0	0.9932	3.09	0.5	9.1	5	0	white	0
42	5.9	0.36	0.04	5.7	0.046	21.0	87.0	0.9934	3.22	0.51	10.2	5	0	white	0
43	5.9	0.65	0.23	5.0	0.035	20.0	128.0	0.99016	3.46	0.48	12.8	6	0	white	0
44	6.0	0.2	0.71	1.6	0.15	10.0	54.0	0.9927	3.12	0.47	9.8	5	0	white	0
45	6.0	0.24	0.27	1.9	0.048	40.0	170.0	0.9938	3.64	0.54	10.0	7	1	white	0
46	6.0	0.26	0.15	1.2	0.053	35.0	124.0	0.99347	3.08	0.46	8.8	5	0	white	0
47	6.0	0.26	0.18	7.0	0.055	50.0	194.0	0.99591	3.21	0.43	9.0	5	0	white	0
48	6.0	0.26	0.26	2.2	0.035	10.0	72.0	0.989465	3.11	0.48	12.15	6	0	white	0
49	6.0	0.27	0.32	3.6	0.035	36.0	133.0	0.99215	3.23	0.46	10.8	6	0	white	0
50	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	0
51	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	0
52	6.0	0.28	0.52	6.2	0.028	37.0	104.0	0.99161	3.28	0.51	11.8	7	1	white	0
53	6.0	0.32	0.3	1.9	0.033	41.0	142.0	0.98912	3.29	0.42	12.8	7	1	white	0
54	6.0	0.36	0.39	3.2	0.027	20.0	125.0	0.991	3.38	0.39	11.3	7	1	white	0
55	6.0	0.42	0.19	2.0	0.075	22.0	47.0	0.99522	3.39	0.78	10.0	6	0	red	0
56	6.0	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	0
57	6.0	0.49	0.0	2.3	0.068	15.0	33.0	0.99292	3.58	0.59	12.5	6	0	red	0
58	6.0	0.58	0.2	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5	6	0	red	0
59	6.0	0.615	0.04	0.8	0.032	8.0	50.0	0.99036	3.14	0.4	11.0	4	0	white	0
60	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	0
61	6.1	0.3	0.56	2.8	0.044	47.0	179.0	0.9924	3.3	0.57	10.9	7	1	white	0
62	6.1	0.32	0.25	1.8	0.086	5.0	32.0	0.99464	3.36	0.44	10.1	5	0	red	0
63	6.1	0.32	0.28	6.6	0.021	29.0	132.0	0.99188	3.15	0.36	11.45	7	1	white	0
64	6.1	0.34	0.24	18.35	0.05	33.0	184.0	0.99943	3.12	0.61	9.3	5	0	white	0
65	6.1	0.4	0.16	1.8	0.069	11.0	25.0	0.9955	3.42	0.74	10.1	7	1	red	0
66	6.1	0.41	0.14	10.4	0.037	18.0	119.0	0.996	3.38	0.45	10.0	5	0	white	0
67	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.3	7	1	white	0
68	6.2	0.16	0.54	1.4	0.126	37.0	110.0	0.9932	3.23	0.37	8.9	6	0	white	0
69	6.2	0.2	0.31	1.0	0.031	22.0	73.0	0.99035	3.24	0.52	11.3	6	0	white	0
70	6.2	0.2	0.33	5.4	0.028	21.0	75.0	0.99012	3.36	0.41	13.5	7	1	white	0
71	6.2	0.22	0.2	20.8	0.035	58.0	184.0	1.00022	3.11	0.53	9.0	6	0	white	0
72	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	0
73	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	0
74	6.2	0.23	0.38	1.6	0.044	12.0	113.0	0.99176	3.3	0.73	11.4	5	0	white	0
75	6.2	0.25	0.44	15.8	0.057	39.0	167.0	0.99804	3.14	0.51	9.2	5	0	white	0
76	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	0
77	6.2	0.3	0.21	1.1	0.032	31.0	111.0	0.9889	2.97	0.42	12.2	6	0	white	0
78	6.2	0.31	0.21	6.3	0.041	50.0	218.0	0.9941	3.15	0.6	10.0	5	0	white	0
79	6.2	0.33	0.29	1.3	0.042	26.0	138.0	0.9956	3.77	0.64	9.5	5	0	white	0
80	6.2	0.35	0.31	2.6	0.036	37.0	92.0	0.98938	3.27	0.53	12.8	7	1	white	0
81	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	0
82	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	0
83	6.2	0.37	0.3	6.6	0.346	79.0	200.0	0.9954	3.29	0.58	9.6	5	0	white	0
84	6.2	0.46	0.25	4.4	0.066	62.0	207.0	0.9939	3.25	0.52	9.8	5	0	white	0
85	6.2	0.46	0.29	2.1	0.074	32.0	98.0	0.99578	3.33	0.62	9.8	5	0	red	0
86	6.2	0.58	0.0	1.6	0.065	8.0	18.0	0.9966	3.56	0.84	9.4	5	0	red	0
87	6.2	0.63	0.31	1.7	0.088	15.0	64.0	0.9969	3.46	0.79	9.3	5	0	red	0
88	6.3	0.15	0.34	11.4	0.05	25.0	96.0	0.99754	3.21	0.49	10.0	6	0	white	0
89	6.3	0.23	0.3	1.8	0.033	16.0	91.0	0.9906	3.28	0.4	11.8	6	0	white	0
90	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white	0
91	6.3	0.26	0.29	2.2	0.043	35.0	175.0	0.9918	3.38	0.43	11.6	6	0	white	0
92	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	0
93	6.3	0.27	0.23	2.9	0.047	13.0	100.0	0.9936	3.28	0.43	9.8	5	0	white	0
94	6.3	0.32	0.35	11.1	0.039	29.0	198.0	0.9984	3.36	0.5	9.4	7	1	white	0
95	6.3	0.33	0.2	17.9	0.066	36.0	161.0	0.9991	3.14	0.51	8.8	5	0	white	0
96	6.3	0.37	0.37	1.5	0.024	12.0	76.0	0.98876	2.94	0.39	12.3	6	0	white	0
97	6.3	0.41	0.33	4.7	0.023	28.0	110.0	0.991	3.3	0.38	12.5	7	1	white	0
98	6.3	1.02	0.0	2.0	0.083	17.0	24.0	0.99437	3.59	0.55	11.2	4	0	red	0
99	6.4	0.15	0.29	1.8	0.044	21.0	115.0	0.99166	3.1	0.38	10.2	5	0	white	0
100	6.4	0.15	0.31	1.1	0.044	25.0	96.0	0.9932	3.54	0.51	10.3	6	0	white	0

Rows: 1-100 | Columns: 15

Note

Predictions can be made automatically using the test set, in which case you don’t need to specify the predictors. Alternatively, you can pass only the vDataFrame to the predict() function, but in this case, it’s essential that the column names of the vDataFrame match the predictors and response name in the model.

Probabilities

It is also easy to get the model’s probabilities:

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

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	Abc prediction Varchar(1)	Abc prediction_0 Varchar(128)	Abc prediction_1 Varchar(128)
1	4.8	0.34	0.0	6.5	0.028	33.0	163.0	0.9939	3.36	0.61	9.9	6	0	white	0	0.898569	0.101431
2	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	0	0.585182	0.414818
3	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	0	0.630081	0.369919
4	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	0	0.603775	0.396225
5	5.1	0.35	0.26	6.8	0.034	36.0	120.0	0.99188	3.38	0.4	11.5	6	0	white	0	0.603775	0.396225
6	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	0	0.758513	0.241487
7	5.2	0.28	0.29	1.1	0.028	18.0	69.0	0.99168	3.24	0.54	10.0	6	0	white	0	0.722039	0.277961
8	5.3	0.31	0.38	10.5	0.031	53.0	140.0	0.99321	3.34	0.46	11.7	6	0	white	0	0.845468	0.154532
9	5.3	0.58	0.07	6.9	0.043	34.0	149.0	0.9944	3.34	0.57	9.7	5	0	white	0	0.895263	0.104737
10	5.4	0.18	0.24	4.8	0.041	30.0	113.0	0.99445	3.42	0.4	9.4	6	0	white	0	0.836464	0.163536
11	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	0.890809	0.109191
12	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	0.630081	0.369919
13	5.6	0.19	0.46	1.1	0.032	33.0	115.0	0.9909	3.36	0.5	10.4	6	0	white	0	0.643849	0.356151
14	5.6	0.23	0.29	3.1	0.023	19.0	89.0	0.99068	3.25	0.51	11.2	6	0	white	0	0.529253	0.470747
15	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	0.617696	0.382304
16	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	0.614657	0.385343
17	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	0.618969	0.381031
18	5.7	0.14	0.3	5.4	0.045	26.0	105.0	0.99469	3.32	0.45	9.3	5	0	white	0	0.828846	0.171154
19	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	0.863451	0.136549
20	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	0.863451	0.136549
21	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	0.863451	0.136549
22	5.7	0.255	0.65	1.2	0.079	17.0	137.0	0.99307	3.2	0.42	9.4	5	0	white	0	0.862273	0.137727
23	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	0.863451	0.136549
24	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	0.611036	0.388964
25	5.8	0.25	0.28	11.1	0.056	45.0	175.0	0.99755	3.42	0.43	9.5	5	0	white	0	0.884316	0.115684
26	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	0.898569	0.101431
27	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	0.751833	0.248167
28	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	0.868822	0.131178
29	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	0.584251	0.415749
30	5.8	0.34	0.16	7.0	0.037	26.0	116.0	0.9949	3.46	0.45	10.0	7	1	white	0	0.898569	0.101431
31	5.8	0.36	0.26	3.3	0.038	40.0	153.0	0.9911	3.34	0.55	11.3	6	0	white	0	0.545069	0.454931
32	5.8	0.36	0.5	1.0	0.127	63.0	178.0	0.99212	3.1	0.45	9.7	5	0	white	0	0.841109	0.158891
33	5.8	0.38	0.26	1.1	0.058	20.0	140.0	0.99271	3.27	0.43	9.7	6	0	white	0	0.879365	0.120635
34	5.9	0.17	0.28	0.7	0.027	5.0	28.0	0.98985	3.13	0.32	10.6	5	0	white	0	0.598842	0.401158
35	5.9	0.17	0.29	3.1	0.03	32.0	123.0	0.98913	3.41	0.33	13.7	7	1	white	0	0.545891	0.454109
36	5.9	0.2	0.23	1.5	0.037	38.0	93.0	0.99021	3.36	0.49	12.0	6	0	white	0	0.611036	0.388964
37	5.9	0.25	0.19	12.4	0.047	50.0	162.0	0.9973	3.35	0.38	9.5	5	0	white	0	0.941882	0.0581183
38	5.9	0.27	0.27	9.0	0.051	43.0	136.0	0.9941	3.25	0.53	10.7	6	0	white	0	0.884316	0.115684
39	5.9	0.28	0.34	3.6	0.04	50.0	194.0	0.9912	3.31	0.52	11.6	6	0	white	0	0.559361	0.440639
40	5.9	0.29	0.25	13.4	0.067	72.0	160.0	0.99721	3.33	0.54	10.3	6	0	red	0	0.941882	0.0581183
41	5.9	0.35	0.47	2.2	0.11	14.0	138.0	0.9932	3.09	0.5	9.1	5	0	white	0	0.862273	0.137727
42	5.9	0.36	0.04	5.7	0.046	21.0	87.0	0.9934	3.22	0.51	10.2	5	0	white	0	0.877653	0.122347
43	5.9	0.65	0.23	5.0	0.035	20.0	128.0	0.99016	3.46	0.48	12.8	6	0	white	0	0.529936	0.470064
44	6.0	0.2	0.71	1.6	0.15	10.0	54.0	0.9927	3.12	0.47	9.8	5	0	white	0	0.868329	0.131671
45	6.0	0.24	0.27	1.9	0.048	40.0	170.0	0.9938	3.64	0.54	10.0	7	1	white	0	0.884316	0.115684
46	6.0	0.26	0.15	1.2	0.053	35.0	124.0	0.99347	3.08	0.46	8.8	5	0	white	0	0.924899	0.0751014
47	6.0	0.26	0.18	7.0	0.055	50.0	194.0	0.99591	3.21	0.43	9.0	5	0	white	0	0.935018	0.0649818
48	6.0	0.26	0.26	2.2	0.035	10.0	72.0	0.989465	3.11	0.48	12.15	6	0	white	0	0.545891	0.454109
49	6.0	0.27	0.32	3.6	0.035	36.0	133.0	0.99215	3.23	0.46	10.8	6	0	white	0	0.617696	0.382304
50	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	0	0.941882	0.0581183
51	6.0	0.28	0.24	17.8	0.047	42.0	111.0	0.99896	3.1	0.45	8.9	6	0	white	0	0.941882	0.0581183
52	6.0	0.28	0.52	6.2	0.028	37.0	104.0	0.99161	3.28	0.51	11.8	7	1	white	0	0.617696	0.382304
53	6.0	0.32	0.3	1.9	0.033	41.0	142.0	0.98912	3.29	0.42	12.8	7	1	white	0	0.531301	0.468699
54	6.0	0.36	0.39	3.2	0.027	20.0	125.0	0.991	3.38	0.39	11.3	7	1	white	0	0.543031	0.456969
55	6.0	0.42	0.19	2.0	0.075	22.0	47.0	0.99522	3.39	0.78	10.0	6	0	red	0	0.929849	0.0701507
56	6.0	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	0	0.872502	0.127498
57	6.0	0.49	0.0	2.3	0.068	15.0	33.0	0.99292	3.58	0.59	12.5	6	0	red	0	0.924899	0.0751014
58	6.0	0.58	0.2	2.4	0.075	15.0	50.0	0.99467	3.58	0.67	12.5	6	0	red	0	0.937275	0.0627251
59	6.0	0.615	0.04	0.8	0.032	8.0	50.0	0.99036	3.14	0.4	11.0	4	0	white	0	0.636947	0.363053
60	6.1	0.28	0.24	19.95	0.074	32.0	174.0	0.99922	3.19	0.44	9.3	6	0	white	0	0.941882	0.0581183
61	6.1	0.3	0.56	2.8	0.044	47.0	179.0	0.9924	3.3	0.57	10.9	7	1	white	0	0.798119	0.201881
62	6.1	0.32	0.25	1.8	0.086	5.0	32.0	0.99464	3.36	0.44	10.1	5	0	red	0	0.929849	0.0701507
63	6.1	0.32	0.28	6.6	0.021	29.0	132.0	0.99188	3.15	0.36	11.45	7	1	white	0	0.587137	0.412863
64	6.1	0.34	0.24	18.35	0.05	33.0	184.0	0.99943	3.12	0.61	9.3	5	0	white	0	0.941882	0.0581183
65	6.1	0.4	0.16	1.8	0.069	11.0	25.0	0.9955	3.42	0.74	10.1	7	1	red	0	0.929849	0.0701507
66	6.1	0.41	0.14	10.4	0.037	18.0	119.0	0.996	3.38	0.45	10.0	5	0	white	0	0.909591	0.0904091
67	6.1	0.44	0.28	4.25	0.032	43.0	132.0	0.9916	3.26	0.47	11.3	7	1	white	0	0.603105	0.396895
68	6.2	0.16	0.54	1.4	0.126	37.0	110.0	0.9932	3.23	0.37	8.9	6	0	white	0	0.820954	0.179046
69	6.2	0.2	0.31	1.0	0.031	22.0	73.0	0.99035	3.24	0.52	11.3	6	0	white	0	0.61548	0.38452
70	6.2	0.2	0.33	5.4	0.028	21.0	75.0	0.99012	3.36	0.41	13.5	7	1	white	0	0.545593	0.454407
71	6.2	0.22	0.2	20.8	0.035	58.0	184.0	1.00022	3.11	0.53	9.0	6	0	white	0	0.84472	0.15528
72	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	0	0.65251	0.34749
73	6.2	0.22	0.28	2.2	0.04	24.0	125.0	0.9917	3.19	0.48	10.5	6	0	white	0	0.65251	0.34749
74	6.2	0.23	0.38	1.6	0.044	12.0	113.0	0.99176	3.3	0.73	11.4	5	0	white	0	0.65251	0.34749
75	6.2	0.25	0.44	15.8	0.057	39.0	167.0	0.99804	3.14	0.51	9.2	5	0	white	0	0.875876	0.124124
76	6.2	0.27	0.32	6.3	0.048	47.0	159.0	0.99282	3.21	0.6	11.0	6	0	white	0	0.850255	0.149745
77	6.2	0.3	0.21	1.1	0.032	31.0	111.0	0.9889	2.97	0.42	12.2	6	0	white	0	0.611036	0.388964
78	6.2	0.31	0.21	6.3	0.041	50.0	218.0	0.9941	3.15	0.6	10.0	5	0	white	0	0.882604	0.117396
79	6.2	0.33	0.29	1.3	0.042	26.0	138.0	0.9956	3.77	0.64	9.5	5	0	white	0	0.857129	0.142871
80	6.2	0.35	0.31	2.6	0.036	37.0	92.0	0.98938	3.27	0.53	12.8	7	1	white	0	0.531301	0.468699
81	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	0	0.511958	0.488042
82	6.2	0.37	0.24	6.1	0.032	19.0	86.0	0.98934	3.04	0.26	13.4	8	1	white	0	0.511958	0.488042
83	6.2	0.37	0.3	6.6	0.346	79.0	200.0	0.9954	3.29	0.58	9.6	5	0	white	0	0.884316	0.115684
84	6.2	0.46	0.25	4.4	0.066	62.0	207.0	0.9939	3.25	0.52	9.8	5	0	white	0	0.935018	0.0649818
85	6.2	0.46	0.29	2.1	0.074	32.0	98.0	0.99578	3.33	0.62	9.8	5	0	red	0	0.894544	0.105456
86	6.2	0.58	0.0	1.6	0.065	8.0	18.0	0.9966	3.56	0.84	9.4	5	0	red	0	0.920055	0.0799455
87	6.2	0.63	0.31	1.7	0.088	15.0	64.0	0.9969	3.46	0.79	9.3	5	0	red	0	0.901969	0.0980305
88	6.3	0.15	0.34	11.4	0.05	25.0	96.0	0.99754	3.21	0.49	10.0	6	0	white	0	0.813956	0.186044
89	6.3	0.23	0.3	1.8	0.033	16.0	91.0	0.9906	3.28	0.4	11.8	6	0	white	0	0.545891	0.454109
90	6.3	0.23	0.49	7.1	0.05	67.0	210.0	0.9951	3.23	0.34	9.5	5	0	white	0	0.867224	0.132776
91	6.3	0.26	0.29	2.2	0.043	35.0	175.0	0.9918	3.38	0.43	11.6	6	0	white	0	0.65251	0.34749
92	6.3	0.27	0.18	7.7	0.048	45.0	186.0	0.9962	3.23	0.47	9.0	5	0	white	0	0.935018	0.0649818
93	6.3	0.27	0.23	2.9	0.047	13.0	100.0	0.9936	3.28	0.43	9.8	5	0	white	0	0.918049	0.0819509
94	6.3	0.32	0.35	11.1	0.039	29.0	198.0	0.9984	3.36	0.5	9.4	7	1	white	0	0.868822	0.131178
95	6.3	0.33	0.2	17.9	0.066	36.0	161.0	0.9991	3.14	0.51	8.8	5	0	white	0	0.941882	0.0581183
96	6.3	0.37	0.37	1.5	0.024	12.0	76.0	0.98876	2.94	0.39	12.3	6	0	white	0	0.598851	0.401149
97	6.3	0.41	0.33	4.7	0.023	28.0	110.0	0.991	3.3	0.38	12.5	7	1	white	0	0.528132	0.471868
98	6.3	1.02	0.0	2.0	0.083	17.0	24.0	0.99437	3.59	0.55	11.2	4	0	red	0	0.937275	0.0627251
99	6.4	0.15	0.29	1.8	0.044	21.0	115.0	0.99166	3.1	0.38	10.2	5	0	white	0	0.65251	0.34749
100	6.4	0.15	0.31	1.1	0.044	25.0	96.0	0.9932	3.54	0.51	10.3	6	0	white	0	0.793147	0.206853

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([[1033,    0],
       [ 263,    0]])

Note

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

Main Plots (Classification Curves)

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

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

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

model.roc_curve()

Important

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

Hint

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

Other Plots

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

model.plot_tree()

Note

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

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

model.to_graphviz()
Out[5]: 'digraph Tree {\ngraph [bgcolor="#FFFFFFDD"];\n0 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.992198", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.992198", color="#000000", fontcolor="#000000"]\n1 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 1.56875", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 1.56875", color="#000000", fontcolor="#000000"]\n2 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.220625", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.220625", color="#000000", fontcolor="#000000"]\n3 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 0.991474", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 0.991474", color="#000000", fontcolor="#000000"]\n4 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n4 -> 9 [label="<= 0.046625", color="#000000", fontcolor="#000000"]\n4 -> 10 [label="> 0.046625", color="#000000", fontcolor="#000000"]\n5 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 11 [label="<= 6.15", color="#000000", fontcolor="#000000"]\n5 -> 12 [label="> 6.15", color="#000000", fontcolor="#000000"]\n6 [label="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 13 [label="<= 0.259375", color="#000000", fontcolor="#000000"]\n6 -> 14 [label="> 0.259375", 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): 0.7</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.3</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): 0.9</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"]\n9 [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): 0.47</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.53</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): 0.74</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.26</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): 0.72</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.28</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): 0.96</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"]\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): 0.86</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"]\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 /notebooks/ml/model_tracking_versioning/index.ipynb for more details on model tracking and versioning.

Model Exporting

To Memmodel

model.to_memmodel()

Note

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

The 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 "density" < 0.992198 THEN (CASE WHEN "residual_sugar" < 1.56875 THEN (CASE WHEN "density" < 0.991474 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "chlorides" < 0.046625 THEN 1.0 ELSE 0.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 6.15 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.992923 THEN (CASE WHEN "volatile_acidity" < 0.314375 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "residual_sugar" < 12.225 THEN (CASE WHEN "citric_acid" < 0.31125 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 6.9 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991474 THEN (CASE WHEN "residual_sugar" < 1.56875 THEN (CASE WHEN "density" < 0.988578 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.415 THEN 1.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.993647 THEN (CASE WHEN "fixed_acidity" < 6.15 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 9.525 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.992198 THEN (CASE WHEN "residual_sugar" < 1.56875 THEN (CASE WHEN "citric_acid" < 0.155625 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "residual_sugar" < 3.50625 THEN 0.0 ELSE 1.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 6.525 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.99075 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.996543 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.992198 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 8.775 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "residual_sugar" < 6.4125 THEN (CASE WHEN "density" < 0.991474 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "residual_sugar" < 2.5375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.408125 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.992198 THEN (CASE WHEN "density" < 0.991474 THEN (CASE WHEN "citric_acid" < 0.31125 THEN 1.0 ELSE 0.0 END) ELSE (CASE WHEN "residual_sugar" < 2.5375 THEN 0.0 ELSE 1.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 6.525 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "chlorides" < 0.027813 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.992923 THEN (CASE WHEN "density" < 0.99075 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "fixed_acidity" < 5.4 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.17375 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "volatile_acidity" < 0.501875 THEN (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "chlorides" < 0.027813 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 8.775 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "chlorides" < 0.065437 THEN (CASE WHEN "volatile_acidity" < 0.595625 THEN 0.0 ELSE 0.0 END) ELSE 0.0 END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "residual_sugar" < 9.31875 THEN (CASE WHEN "density" < 0.992198 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995819 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "citric_acid" < 0.466875 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.31125 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