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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: int | float | Decimal = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 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 at training-time and outputting a class with the mode.

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,
)

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	0.7526598414132288
prc_auc	0.43951769633060406
accuracy	0.8121632024634334
log_loss	0.183867356058981
precision	0.6666666666666666
recall	0.016260162601626018
f1_score	0.03174603174603175
mcc	0.08298246149809468
informedness	0.014360827368957496
markedness	0.47950502706883213
csi	0.016129032258064516

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	0.7526598414132288
prc_auc	0.43951769633060406
accuracy	0.6828329484218629
log_loss	0.183867356058981
precision	0.3359683794466403
recall	0.6910569105691057
f1_score	0.4521276595744681
mcc	0.2988657565366034
informedness	0.3719685914807864
markedness	0.240129791804774
csi	0.2920962199312715

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]: 0.8121632024634334

Prediction

Prediction is straight-forward:

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

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	Abc prediction Varchar(1)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white	0
2	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	0
3	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
4	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	0
5	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	0
6	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
7	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	0
8	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white	0
9	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	0
10	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	0
11	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red	0
12	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
13	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	0
14	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	0
15	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	1
16	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	1
17	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	0
18	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	0
19	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	0
20	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
21	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red	0
22	5.2	0.34	0.37	6.2	0.031	42.0	133.0	0.99076	3.25	0.41	12.5	6	0	white	0
23	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
24	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	0
25	5.2	0.645	0.0	2.15	0.08	15.0	28.0	0.99444	3.78	0.61	12.5	6	0	red	0
26	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	0
27	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
28	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
29	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red	0
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	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	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	0.29	0.47	3.0	0.052	47.0	145.0	0.993	3.29	0.75	10.0	6	0	white	0
35	5.4	0.375	0.4	3.3	0.054	29.0	147.0	0.99482	3.42	0.52	9.1	5	0	white	0
36	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	0
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	0.74	0.09	1.7	0.089	16.0	26.0	0.99402	3.67	0.56	11.6	6	0	red	0
39	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white	0
40	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0
41	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0
42	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	0
43	5.5	0.28	0.21	1.6	0.032	23.0	85.0	0.99027	3.42	0.42	12.5	5	0	white	0
44	5.5	0.315	0.38	2.6	0.033	10.0	69.0	0.9909	3.12	0.59	10.8	6	0	white	0
45	5.5	0.335	0.3	2.5	0.071	27.0	128.0	0.9924	3.14	0.51	9.6	6	0	white	0
46	5.5	0.34	0.26	2.2	0.021	31.0	119.0	0.98919	3.55	0.49	13.0	8	1	white	0
47	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
48	5.6	0.12	0.33	2.9	0.044	21.0	73.0	0.98896	3.17	0.32	12.9	8	1	white	0
49	5.6	0.18	0.58	1.25	0.034	29.0	129.0	0.98984	3.51	0.6	12.0	7	1	white	0
50	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white	0
51	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
52	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	0
53	5.6	0.2	0.22	1.3	0.049	25.0	155.0	0.99296	3.74	0.43	10.0	5	0	white	0
54	5.6	0.21	0.4	1.3	0.041	81.0	147.0	0.9901	3.22	0.95	11.6	8	1	white	0
55	5.6	0.245	0.25	9.7	0.032	12.0	68.0	0.994	3.31	0.34	10.5	5	0	white	0
56	5.6	0.26	0.27	10.6	0.03	27.0	119.0	0.9947	3.4	0.34	10.7	7	1	white	0
57	5.6	0.27	0.37	0.9	0.025	11.0	49.0	0.98845	3.29	0.33	13.1	6	0	white	0
58	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	0
59	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0
60	5.6	0.31	0.78	13.9	0.074	23.0	92.0	0.99677	3.39	0.48	10.5	6	0	red	0
61	5.6	0.34	0.3	6.9	0.038	23.0	89.0	0.99266	3.25	0.49	11.1	6	0	white	0
62	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	0
63	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
64	5.6	0.62	0.03	1.5	0.08	6.0	13.0	0.99498	3.66	0.62	10.1	4	0	red	0
65	5.6	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	0
66	5.7	0.18	0.22	4.2	0.042	25.0	111.0	0.994	3.35	0.39	9.4	5	0	white	0
67	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white	0
68	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	0
69	5.7	0.21	0.25	1.1	0.035	26.0	81.0	0.9902	3.31	0.52	11.4	6	0	white	0
70	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
71	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
72	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
73	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
74	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
75	5.7	0.26	0.25	10.4	0.02	7.0	57.0	0.994	3.39	0.37	10.6	5	0	white	0
76	5.7	0.28	0.28	2.2	0.019	15.0	65.0	0.9902	3.06	0.52	11.2	6	0	white	0
77	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
78	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
79	5.7	0.4	0.35	5.1	0.026	17.0	113.0	0.99052	3.18	0.67	12.4	6	0	white	0
80	5.7	0.41	0.21	1.9	0.048	30.0	112.0	0.99138	3.29	0.55	11.2	6	0	white	0
81	5.7	1.13	0.09	1.5	0.172	7.0	19.0	0.994	3.5	0.48	9.8	4	0	red	0
82	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
83	5.8	0.15	0.28	0.8	0.037	43.0	127.0	0.99198	3.24	0.51	9.3	5	0	white	0
84	5.8	0.19	0.24	1.3	0.044	38.0	128.0	0.99362	3.77	0.6	10.6	5	0	white	0
85	5.8	0.2	0.34	1.0	0.035	40.0	86.0	0.98993	3.5	0.42	11.7	5	0	white	0
86	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
87	5.8	0.23	0.2	2.0	0.043	39.0	154.0	0.99226	3.21	0.39	10.2	6	0	white	0
88	5.8	0.23	0.27	1.8	0.043	24.0	69.0	0.9933	3.38	0.31	9.4	6	0	white	0
89	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
90	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0
91	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0
92	5.8	0.27	0.22	12.7	0.058	42.0	206.0	0.9946	3.32	0.38	12.3	6	0	white	0
93	5.8	0.275	0.3	5.4	0.043	41.0	149.0	0.9926	3.33	0.42	10.8	7	1	white	0
94	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	0
95	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0
96	5.8	0.3	0.23	1.5	0.034	37.0	121.0	0.98871	2.96	0.34	12.1	6	0	white	0
97	5.8	0.31	0.32	4.5	0.024	28.0	94.0	0.98906	3.25	0.52	13.7	7	1	white	0
98	5.8	0.32	0.28	4.3	0.032	46.0	115.0	0.98946	3.16	0.57	13.0	8	1	white	0
99	5.8	0.32	0.31	2.7	0.049	25.0	153.0	0.99067	3.44	0.73	12.2	7	1	white	0
100	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	0

Rows: 1-100 | Columns: 15

Note

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

Probabilities

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

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

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	Abc prediction Varchar(1)	Abc prediction_0 Varchar(128)	Abc prediction_1 Varchar(128)
1	3.8	0.31	0.02	11.1	0.036	20.0	114.0	0.99248	3.75	0.44	12.4	6	0	white	0	0.717743	0.282257
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	0	0.534054	0.465946
3	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	0.717743	0.282257
4	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	0	0.855036	0.144964
5	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	0	0.855036	0.144964
6	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	0.675274	0.324726
7	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	0	0.855036	0.144964
8	5.0	0.33	0.16	1.5	0.049	10.0	97.0	0.9917	3.48	0.44	10.7	6	0	white	0	0.763785	0.236215
9	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	0	0.722592	0.277408
10	5.0	0.35	0.25	7.8	0.031	24.0	116.0	0.99241	3.39	0.4	11.3	6	0	white	0	0.722592	0.277408
11	5.0	0.4	0.5	4.3	0.046	29.0	80.0	0.9902	3.49	0.66	13.6	6	0	red	0	0.52832	0.47168
12	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	0.754978	0.245022
13	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	0	0.584548	0.415452
14	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	0	0.679197	0.320803
15	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	1	0.495415	0.504585
16	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	1	0.495415	0.504585
17	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	0	0.766075	0.233925
18	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	0	0.692399	0.307601
19	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	0	0.855036	0.144964
20	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	0.653614	0.346386
21	5.2	0.32	0.25	1.8	0.103	13.0	50.0	0.9957	3.38	0.55	9.2	5	0	red	0	0.920678	0.0793224
22	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	0	0.532585	0.467415
23	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	0.901263	0.098737
24	5.2	0.44	0.04	1.4	0.036	43.0	119.0	0.9894	3.36	0.33	12.1	8	1	white	0	0.610506	0.389494
25	5.2	0.645	0.0	2.15	0.08	15.0	28.0	0.99444	3.78	0.61	12.5	6	0	red	0	0.880317	0.119683
26	5.3	0.2	0.31	3.6	0.036	22.0	91.0	0.99278	3.41	0.5	9.8	6	0	white	0	0.68977	0.31023
27	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	0.646058	0.353942
28	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	0.646058	0.353942
29	5.3	0.715	0.19	1.5	0.161	7.0	62.0	0.99395	3.62	0.61	11.0	5	0	red	0	0.880317	0.119683
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	0.766075	0.233925
31	5.4	0.255	0.33	1.2	0.051	29.0	122.0	0.99048	3.37	0.66	11.3	6	0	white	0	0.756997	0.243003
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	0.632458	0.367542
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	0.632458	0.367542
34	5.4	0.29	0.47	3.0	0.052	47.0	145.0	0.993	3.29	0.75	10.0	6	0	white	0	0.802419	0.197581
35	5.4	0.375	0.4	3.3	0.054	29.0	147.0	0.99482	3.42	0.52	9.1	5	0	white	0	0.865274	0.134726
36	5.4	0.46	0.15	2.1	0.026	29.0	130.0	0.98953	3.39	0.77	13.4	8	1	white	0	0.542114	0.457886
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	0.517293	0.482707
38	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	0	0.880317	0.119683
39	5.5	0.16	0.26	1.5	0.032	35.0	100.0	0.99076	3.43	0.77	12.0	6	0	white	0	0.653614	0.346386
40	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0	0.757702	0.242298
41	5.5	0.17	0.23	2.9	0.039	10.0	108.0	0.99243	3.28	0.5	10.0	5	0	white	0	0.757702	0.242298
42	5.5	0.23	0.19	2.2	0.044	39.0	161.0	0.99209	3.19	0.43	10.4	6	0	white	0	0.781536	0.218464
43	5.5	0.28	0.21	1.6	0.032	23.0	85.0	0.99027	3.42	0.42	12.5	5	0	white	0	0.642243	0.357757
44	5.5	0.315	0.38	2.6	0.033	10.0	69.0	0.9909	3.12	0.59	10.8	6	0	white	0	0.625916	0.374084
45	5.5	0.335	0.3	2.5	0.071	27.0	128.0	0.9924	3.14	0.51	9.6	6	0	white	0	0.829462	0.170538
46	5.5	0.34	0.26	2.2	0.021	31.0	119.0	0.98919	3.55	0.49	13.0	8	1	white	0	0.599555	0.400445
47	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	0.625916	0.374084
48	5.6	0.12	0.33	2.9	0.044	21.0	73.0	0.98896	3.17	0.32	12.9	8	1	white	0	0.522179	0.477821
49	5.6	0.18	0.58	1.25	0.034	29.0	129.0	0.98984	3.51	0.6	12.0	7	1	white	0	0.647752	0.352248
50	5.6	0.185	0.49	1.1	0.03	28.0	117.0	0.9918	3.55	0.45	10.3	6	0	white	0	0.680783	0.319217
51	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	0.653614	0.346386
52	5.6	0.19	0.27	0.9	0.04	52.0	103.0	0.99026	3.5	0.39	11.2	5	0	white	0	0.620583	0.379417
53	5.6	0.2	0.22	1.3	0.049	25.0	155.0	0.99296	3.74	0.43	10.0	5	0	white	0	0.863764	0.136236
54	5.6	0.21	0.4	1.3	0.041	81.0	147.0	0.9901	3.22	0.95	11.6	8	1	white	0	0.632458	0.367542
55	5.6	0.245	0.25	9.7	0.032	12.0	68.0	0.994	3.31	0.34	10.5	5	0	white	0	0.834839	0.165161
56	5.6	0.26	0.27	10.6	0.03	27.0	119.0	0.9947	3.4	0.34	10.7	7	1	white	0	0.814464	0.185536
57	5.6	0.27	0.37	0.9	0.025	11.0	49.0	0.98845	3.29	0.33	13.1	6	0	white	0	0.632458	0.367542
58	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	0	0.694546	0.305454
59	5.6	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0	0.89111	0.10889
60	5.6	0.31	0.78	13.9	0.074	23.0	92.0	0.99677	3.39	0.48	10.5	6	0	red	0	0.89111	0.10889
61	5.6	0.34	0.3	6.9	0.038	23.0	89.0	0.99266	3.25	0.49	11.1	6	0	white	0	0.650906	0.349094
62	5.6	0.5	0.09	2.3	0.049	17.0	99.0	0.9937	3.63	0.63	13.0	5	0	red	0	0.880317	0.119683
63	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	0.880317	0.119683
64	5.6	0.62	0.03	1.5	0.08	6.0	13.0	0.99498	3.66	0.62	10.1	4	0	red	0	0.880317	0.119683
65	5.6	0.695	0.06	6.8	0.042	9.0	84.0	0.99432	3.44	0.44	10.2	5	0	white	0	0.855036	0.144964
66	5.7	0.18	0.22	4.2	0.042	25.0	111.0	0.994	3.35	0.39	9.4	5	0	white	0	0.844557	0.155443
67	5.7	0.18	0.26	2.2	0.023	21.0	95.0	0.9893	3.07	0.54	12.3	6	0	white	0	0.620583	0.379417
68	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	0	0.736874	0.263126
69	5.7	0.21	0.25	1.1	0.035	26.0	81.0	0.9902	3.31	0.52	11.4	6	0	white	0	0.642243	0.357757
70	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	0.653614	0.346386
71	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	0.879213	0.120787
72	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	0.879213	0.120787
73	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	0.879213	0.120787
74	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	0.949014	0.0509856
75	5.7	0.26	0.25	10.4	0.02	7.0	57.0	0.994	3.39	0.37	10.6	5	0	white	0	0.809651	0.190349
76	5.7	0.28	0.28	2.2	0.019	15.0	65.0	0.9902	3.06	0.52	11.2	6	0	white	0	0.620583	0.379417
77	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	0.756997	0.243003
78	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	0.532585	0.467415
79	5.7	0.4	0.35	5.1	0.026	17.0	113.0	0.99052	3.18	0.67	12.4	6	0	white	0	0.52071	0.47929
80	5.7	0.41	0.21	1.9	0.048	30.0	112.0	0.99138	3.29	0.55	11.2	6	0	white	0	0.738208	0.261792
81	5.7	1.13	0.09	1.5	0.172	7.0	19.0	0.994	3.5	0.48	9.8	4	0	red	0	0.908654	0.0913461
82	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	0.904321	0.0956787
83	5.8	0.15	0.28	0.8	0.037	43.0	127.0	0.99198	3.24	0.51	9.3	5	0	white	0	0.736874	0.263126
84	5.8	0.19	0.24	1.3	0.044	38.0	128.0	0.99362	3.77	0.6	10.6	5	0	white	0	0.844557	0.155443
85	5.8	0.2	0.34	1.0	0.035	40.0	86.0	0.98993	3.5	0.42	11.7	5	0	white	0	0.620583	0.379417
86	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	0.762502	0.237498
87	5.8	0.23	0.2	2.0	0.043	39.0	154.0	0.99226	3.21	0.39	10.2	6	0	white	0	0.793015	0.206985
88	5.8	0.23	0.27	1.8	0.043	24.0	69.0	0.9933	3.38	0.31	9.4	6	0	white	0	0.74165	0.25835
89	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	0.901037	0.0989632
90	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0	0.675274	0.324726
91	5.8	0.26	0.18	1.2	0.031	40.0	114.0	0.9908	3.42	0.4	11.0	7	1	white	0	0.675274	0.324726
92	5.8	0.27	0.22	12.7	0.058	42.0	206.0	0.9946	3.32	0.38	12.3	6	0	white	0	0.888457	0.111543
93	5.8	0.275	0.3	5.4	0.043	41.0	149.0	0.9926	3.33	0.42	10.8	7	1	white	0	0.694546	0.305454
94	5.8	0.28	0.66	9.1	0.039	26.0	159.0	0.9965	3.66	0.55	10.8	5	0	white	0	0.896182	0.103818
95	5.8	0.3	0.12	1.6	0.036	57.0	163.0	0.99239	3.38	0.59	10.5	6	0	white	0	0.793015	0.206985
96	5.8	0.3	0.23	1.5	0.034	37.0	121.0	0.98871	2.96	0.34	12.1	6	0	white	0	0.642243	0.357757
97	5.8	0.31	0.32	4.5	0.024	28.0	94.0	0.98906	3.25	0.52	13.7	7	1	white	0	0.522179	0.477821
98	5.8	0.32	0.28	4.3	0.032	46.0	115.0	0.98946	3.16	0.57	13.0	8	1	white	0	0.501151	0.498849
99	5.8	0.32	0.31	2.7	0.049	25.0	153.0	0.99067	3.44	0.73	12.2	7	1	white	0	0.679356	0.320644
100	5.8	0.345	0.15	10.8	0.033	26.0	120.0	0.99494	3.25	0.49	10.0	6	0	white	0	0.846318	0.153682

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([[1053,    0],
       [ 246,    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="#FFFFFF00"];\n0 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n0 -> 1 [label="<= 0.991973", color="#666666", fontcolor="#666666"]\n0 -> 2 [label="> 0.991973", color="#666666", fontcolor="#666666"]\n1 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n1 -> 3 [label="<= 2.6375", color="#666666", fontcolor="#666666"]\n1 -> 4 [label="> 2.6375", color="#666666", fontcolor="#666666"]\n2 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n2 -> 5 [label="<= 0.220625", color="#666666", fontcolor="#666666"]\n2 -> 6 [label="> 0.220625", color="#666666", fontcolor="#666666"]\n3 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n3 -> 7 [label="<= 6.759375", color="#666666", fontcolor="#666666"]\n3 -> 8 [label="> 6.759375", color="#666666", fontcolor="#666666"]\n4 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n4 -> 9 [label="<= 6.7125", color="#666666", fontcolor="#666666"]\n4 -> 10 [label="> 6.7125", color="#666666", fontcolor="#666666"]\n5 [label="\\"fixed_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n5 -> 11 [label="<= 6.39375", color="#666666", fontcolor="#666666"]\n5 -> 12 [label="> 6.39375", color="#666666", fontcolor="#666666"]\n6 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFF00", fontcolor="#666666", color="#666666"]\n6 -> 13 [label="<= 0.995215", color="#666666", fontcolor="#666666"]\n6 -> 14 [label="> 0.995215", color="#666666", fontcolor="#666666"]\n7 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.59</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.41</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n8 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.74</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.26</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n9 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#efc5b5" color="#666666"><FONT color="#000000"><b>prediction: 1 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.48</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.52</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n10 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#efc5b5" color="#666666"><FONT color="#000000"><b>prediction: 1 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.23</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.77</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n11 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.89</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.11</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n12 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.75</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.25</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n13 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.83</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.17</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\n14 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#666666"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#666666"><FONT color="#666666">prob(0): 0.94</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#666666"><FONT color="#666666">prob(1): 0.06</FONT></td></tr></table>>, fillcolor="#FFFFFF00", fontcolor="#666666", shape="none", color="#666666"]\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 "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN (CASE WHEN "fixed_acidity" < 6.759375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "residual_sugar" < 6.7125 THEN 1.0 ELSE 1.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "fixed_acidity" < 6.39375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995215 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "residual_sugar" < 4.675 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995215 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "volatile_acidity" < 0.126875 THEN 1.0 ELSE 0.0 END) END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN (CASE WHEN "density" < 0.996836 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" < 0.991973 THEN (CASE WHEN "volatile_acidity" < 0.314375 THEN (CASE WHEN "density" < 0.990352 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "citric_acid" < 0.10375 THEN 0.0 ELSE 1.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE (CASE WHEN "density" < 0.993594 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "density" < 0.990352 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.995215 THEN (CASE WHEN "residual_sugar" < 8.75 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" < 0.046625 THEN (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995215 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.995215 THEN 0.0 ELSE (CASE WHEN "citric_acid" < 0.259375 THEN 0.0 ELSE 0.0 END) END) END) + (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "citric_acid" < 0.363125 THEN 0.0 ELSE 0.0 END) ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "residual_sugar" < 6.7125 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" < 0.046625 THEN (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "volatile_acidity" < 0.314375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "density" < 0.995215 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "citric_acid" < 0.259375 THEN (CASE WHEN "fixed_acidity" < 5.6625 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 "citric_acid" < 0.259375 THEN (CASE WHEN "chlorides" < 0.027813 THEN 1.0 ELSE (CASE WHEN "chlorides" < 0.046625 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "chlorides" < 0.046625 THEN (CASE WHEN "citric_acid" < 0.466875 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 "citric_acid" < 0.259375 THEN (CASE WHEN "density" < 0.991973 THEN (CASE WHEN "volatile_acidity" < 0.408125 THEN 0.0 ELSE 1.0 END) ELSE (CASE WHEN "fixed_acidity" < 5.6625 THEN 0.0 ELSE 0.0 END) END) ELSE (CASE WHEN "density" < 0.993594 THEN (CASE WHEN "residual_sugar" < 2.6375 THEN 0.0 ELSE 0.0 END) ELSE (CASE WHEN "fixed_acidity" < 6.759375 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: int | float | Decimal = 1000000000.0, sample: float = 0.632, max_depth: int = 5, min_samples_leaf: int = 1, min_info_gain: int | float | Decimal = 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