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verticapy.machine_learning.vertica.tree.DecisionTreeClassifier

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

A DecisionTreeClassifier consisting of a single tree.

Parameters

name: str, optional

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

overwrite_model: bool, optional

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

max_features: str / int, optional

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

• auto:

  square root of the total number of predictors.

• max:

  number of predictors.

max_leaf_nodes: PythonNumber, optional

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

max_depth: int, optional

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

min_samples_leaf: int, optional

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

min_info_gain: PythonNumber, optional

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

nbins: int, optional

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

Attributes

Many attributes are created during the fitting phase.

trees_: list of one BinaryTreeClassifier

One tree model which is instance of BinaryTreeClassifier. It possess various attributes. For more detailed information, refer to the documentation for BinaryTreeClassifier().

features_importance_: numpy.array

The importance of features. It is calculated using the MDI (Mean Decreased Impurity). To determine the final score, VerticaPy sums the scores of each tree, normalizes them and applies an activation function to scale them. It is necessary to use the features_importance() method to compute it initially, and the computed values will be subsequently utilized for subsequent calls.

classes_: numpy.array

The classes labels.

Note

All attributes can be accessed using the get_attributes() method.

Note

Several other attributes can be accessed by using the get_vertica_attributes() method.

Examples

The following examples provide a basic understanding of usage. For more detailed examples, please refer to the Machine Learning or the Examples section on the website.

Important

Many tree-based models inherit from the RandomForest base class, and it’s recommended to use it directly for access to a wider range of options.

Load data for machine learning

We import verticapy:

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from verticapy are used as intended without interfering with functions from other libraries.

For this example, we will use the winequality dataset.

import verticapy.datasets as vpd

data = vpd.load_winequality()

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)
1	3.9	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 DecisionTreeClassifier model:

from verticapy.machine_learning.vertica import DecisionTreeClassifier

Then we can create the model:

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

Hint

In verticapy 1.0.x and higher, you do not need to specify the model name, as the name is automatically assigned. If you need to re-use the model, you can fetch the model name from the model’s attributes.

Important

The model name is crucial for the model management system and versioning. It’s highly recommended to provide a name if you plan to reuse the model later.

Model Training

We can now fit the model:

model.fit(
    train,
    [
        "fixed_acidity",
        "volatile_acidity",
        "citric_acid",
        "residual_sugar",
        "chlorides",
        "density"
    ],
    "good",
    test,
)



===========
call_string
===========
SELECT rf_classifier('"public"."_verticapy_tmp_randomforestclassifier_v_mldb_566ab6b8979c11efa8720242ac120002_"', '"public"."_verticapy_tmp_view_v_mldb_569984ac979c11efa8720242ac120002_"', 'good', '"fixed_acidity", "volatile_acidity", "citric_acid", "residual_sugar", "chlorides", "density"' USING PARAMETERS exclude_columns='', ntree=1, mtry=3, sampling_size=1, max_depth=3, max_breadth=32, min_leaf_size=5, min_info_gain=0, nbins=32);

=======
details
=======
   predictor    |      type      
----------------+----------------
 fixed_acidity  |float or numeric
volatile_acidity|float or numeric
  citric_acid   |float or numeric
 residual_sugar |float or numeric
   chlorides    |float or numeric
    density     |float or numeric


===============
Additional Info
===============
       Name       |Value
------------------+-----
    tree_count    |  1  
rejected_row_count|  0  
accepted_row_count|5194 

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	0.7415734989648035
prc_auc	0.4117284620513243
accuracy	0.8066001534919417
log_loss	0.186166875398692
precision	0.5333333333333333
recall	0.03162055335968379
f1_score	0.059701492537313425
mcc	0.09253148836995512
informedness	0.024953886693017013
markedness	0.34311594202898554
csi	0.03076923076923077

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	0.7415734989648035
prc_auc	0.4117284620513243
accuracy	0.6999232540291634
log_loss	0.186166875398692
precision	0.35802469135802467
recall	0.6877470355731226
f1_score	0.4709066305818673
mcc	0.3194939450828974
informedness	0.3906041784302654
markedness	0.2613294649198361
csi	0.30796460176991153

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]: 0.8066001534919417

Prediction

Prediction is straight-forward:

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

	123 fixed_acidity Numeric(8)	123 volatile_acidity Numeric(9)	123 citric_acid Numeric(8)	123 residual_sugar Numeric(9)	123 chlorides Float(22)	123 free_sulfur_dioxide Numeric(9)	123 total_sulfur_dioxide Numeric(9)	123 density Float(22)	123 pH Numeric(8)	123 sulphates Numeric(8)	123 alcohol Float(22)	123 quality Integer	123 good Integer	Abc color Varchar(20)	Abc prediction Varchar(1)
1	4.7	0.6	0.17	2.3	0.058	17.0	106.0	0.9932	3.85	0.6	12.9	6	0	red	0
2	5.0	0.27	0.32	4.5	0.032	58.0	178.0	0.98956	3.45	0.31	12.6	7	1	white	0
3	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
4	5.0	0.61	0.12	1.3	0.009	65.0	100.0	0.9874	3.26	0.37	13.5	5	0	white	0
5	5.1	0.21	0.28	1.4	0.047	48.0	148.0	0.99168	3.5	0.49	10.4	5	0	white	0
6	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
7	5.1	0.47	0.02	1.3	0.034	18.0	44.0	0.9921	3.9	0.62	12.8	6	0	red	0
8	5.2	0.6	0.07	7.0	0.044	33.0	147.0	0.9944	3.33	0.58	9.7	5	0	white	0
9	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
10	5.3	0.24	0.33	1.3	0.033	25.0	97.0	0.9906	3.59	0.38	11.0	8	1	white	0
11	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
12	5.5	0.24	0.32	8.7	0.06	19.0	102.0	0.994	3.27	0.31	10.4	5	0	white	0
13	5.6	0.18	0.31	1.5	0.038	16.0	84.0	0.9924	3.34	0.58	10.1	6	0	white	0
14	5.6	0.225	0.24	9.8	0.054	59.0	140.0	0.99545	3.17	0.39	10.2	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	0.31	0.37	1.4	0.074	12.0	96.0	0.9954	3.32	0.58	9.2	5	0	red	0
17	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
18	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
19	5.7	0.31	0.29	7.3	0.05	33.0	143.0	0.99332	3.31	0.5	11.0666666666667	6	0	white	0
20	5.7	0.32	0.5	2.6	0.049	17.0	155.0	0.9927	3.22	0.64	10.0	6	0	white	0
21	5.7	0.33	0.15	1.9	0.05	20.0	93.0	0.9934	3.38	0.62	9.9	5	0	white	0
22	5.7	0.45	0.42	1.1	0.051	61.0	197.0	0.9932	3.02	0.4	9.0	5	0	white	0
23	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
24	5.8	0.28	0.27	2.6	0.054	30.0	156.0	0.9914	3.53	0.42	12.4	5	0	white	0
25	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
26	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
27	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
28	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
29	5.9	0.23	0.28	8.6	0.046	37.0	142.0	0.99432	3.23	0.53	10.6	6	0	white	0
30	5.9	0.24	0.3	2.0	0.033	28.0	92.0	0.99225	3.39	0.69	10.9	7	1	white	0
31	5.9	0.24	0.3	2.0	0.033	28.0	92.0	0.99225	3.39	0.69	10.9	7	1	white	0
32	5.9	0.25	0.27	1.5	0.029	37.0	81.0	0.9892	3.2	0.46	12.2	6	0	white	0
33	5.9	0.415	0.13	1.4	0.04	11.0	64.0	0.9922	3.29	0.52	10.5	5	0	white	0
34	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
35	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
36	6.0	0.28	0.27	4.1	0.046	50.0	147.0	0.99126	3.27	0.56	11.6	6	0	white	0
37	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
38	6.0	0.34	0.66	15.9	0.046	26.0	164.0	0.9979	3.14	0.5	8.8	6	0	white	0
39	6.0	0.38	0.26	6.0	0.034	42.0	134.0	0.9912	3.38	0.38	12.3	7	1	white	0
40	6.0	0.42	0.41	12.4	0.032	50.0	179.0	0.99622	3.14	0.6	9.7	5	0	white	0
41	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
42	6.1	0.16	0.27	12.6	0.064	63.0	162.0	0.9994	3.66	0.43	8.9	5	0	white	0
43	6.1	0.22	0.38	2.8	0.144	12.0	65.0	0.9908	2.95	0.64	11.4	6	0	white	0
44	6.1	0.26	0.51	2.2	0.05	61.0	154.0	0.9929	3.08	0.6	9.8	6	0	white	0
45	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
46	6.1	0.34	0.46	4.7	0.029	21.0	94.0	0.991	3.29	0.62	12.3	6	0	white	0
47	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
48	6.2	0.16	0.33	1.1	0.057	21.0	82.0	0.991	3.32	0.46	10.9	7	1	white	0
49	6.2	0.19	0.38	5.1	0.019	22.0	82.0	0.98961	3.05	0.36	12.5	6	0	white	0
50	6.2	0.2	0.25	15.0	0.055	8.0	120.0	0.99767	3.19	0.53	9.6	6	0	white	0
51	6.2	0.2	0.28	1.1	0.039	24.0	78.0	0.9899	3.36	0.47	12.1	6	0	white	0
52	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
53	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
54	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
55	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
56	6.2	0.29	0.29	5.6	0.046	35.0	178.0	0.99313	3.25	0.51	10.5333333333333	5	0	white	0
57	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
58	6.2	0.3	0.31	1.2	0.048	19.0	125.0	0.98999	3.32	0.54	12.6	6	0	white	0
59	6.2	0.3	0.32	1.7	0.032	30.0	130.0	0.9911	3.28	0.41	11.2	7	1	white	0
60	6.2	0.31	0.26	4.8	0.037	36.0	148.0	0.9928	3.21	0.41	10.4	6	0	white	0
61	6.2	0.32	0.5	6.5	0.048	61.0	186.0	0.9948	3.19	0.45	9.6	5	0	white	0
62	6.2	0.36	0.22	5.25	0.038	44.0	145.0	0.99184	3.22	0.4	11.2	6	0	white	0
63	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	1
64	6.2	0.53	0.02	0.9	0.035	6.0	81.0	0.99234	3.24	0.35	9.5	4	0	white	0
65	6.2	0.56	0.09	1.7	0.053	24.0	32.0	0.99402	3.54	0.6	11.3	5	0	red	0
66	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
67	6.3	0.2	0.26	1.6	0.027	36.0	141.0	0.99268	3.53	0.56	10.8	6	0	white	0
68	6.3	0.22	0.3	2.0	0.05	23.0	120.0	0.99204	3.24	0.47	10.4	6	0	white	0
69	6.3	0.22	0.33	1.7	0.041	67.0	164.0	0.9928	3.32	0.56	10.4	6	0	white	0
70	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
71	6.3	0.25	0.23	14.9	0.039	47.0	142.0	0.99705	3.14	0.35	9.7	6	0	white	0
72	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
73	6.3	0.27	0.46	11.75	0.037	61.0	212.0	0.9971	3.25	0.53	9.5	6	0	white	0
74	6.3	0.28	0.29	6.8	0.051	40.0	143.0	0.99374	3.43	0.59	11.0	6	0	white	0
75	6.3	0.29	0.28	4.7	0.059	28.0	81.0	0.99036	3.24	0.56	12.7	8	1	white	0
76	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
77	6.3	0.41	0.18	3.5	0.027	23.0	109.0	0.99018	3.34	0.54	12.8	8	1	white	1
78	6.3	0.41	0.3	3.2	0.03	49.0	164.0	0.9927	3.53	0.79	11.7	7	1	white	0
79	6.3	0.48	0.04	1.1	0.046	30.0	99.0	0.9928	3.24	0.36	9.6	6	0	white	0
80	6.4	0.14	0.31	1.2	0.034	53.0	138.0	0.99084	3.38	0.35	11.5	7	1	white	0
81	6.4	0.22	0.31	13.9	0.04	57.0	135.0	0.99672	3.21	0.38	10.7	5	0	white	0
82	6.4	0.25	0.32	0.9	0.034	40.0	114.0	0.99114	3.31	0.58	10.8	7	1	white	0
83	6.4	0.27	0.29	10.8	0.028	17.0	118.0	0.99356	3.18	0.37	11.2	6	0	white	0
84	6.4	0.27	0.32	4.5	0.24	61.0	174.0	0.9948	3.12	0.48	9.4	5	0	white	0
85	6.4	0.31	0.28	1.5	0.037	12.0	119.0	0.9919	3.32	0.51	10.4	7	1	white	0
86	6.4	0.41	0.01	6.1	0.048	20.0	70.0	0.99362	3.19	0.42	10.0	5	0	white	0
87	6.5	0.18	0.48	18.0	0.054	56.0	183.0	1.00038	2.98	0.61	8.5	6	0	white	0
88	6.5	0.21	0.35	5.7	0.043	47.0	197.0	0.99392	3.24	0.5	10.1	6	0	white	0
89	6.5	0.22	0.29	7.4	0.028	16.0	87.0	0.99311	3.15	0.56	10.9	7	1	white	0
90	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0
91	6.5	0.23	0.36	16.3	0.038	43.0	133.0	0.99924	3.26	0.41	8.8	5	0	white	0
92	6.5	0.24	0.39	17.3	0.052	22.0	126.0	0.99888	3.11	0.47	9.2	6	0	white	0
93	6.5	0.25	0.32	9.9	0.045	41.0	128.0	0.99636	3.18	0.52	9.6	6	0	white	0
94	6.5	0.28	0.28	8.5	0.047	54.0	210.0	0.9962	3.09	0.54	8.9	4	0	white	0
95	6.5	0.32	0.23	1.2	0.054	39.0	208.0	0.99272	3.18	0.46	9.9	6	0	white	0
96	6.5	0.36	0.38	10.2	0.028	20.0	82.0	0.99274	3.1	0.43	12.1	7	1	white	0
97	6.5	0.4	0.31	3.5	0.046	22.0	147.0	0.99024	3.15	0.31	12.0	7	1	white	0
98	6.5	0.52	0.11	1.8	0.073	13.0	38.0	0.9955	3.34	0.52	9.3	5	0	red	0
99	6.5	0.52	0.17	1.4	0.047	5.0	26.0	0.9932	3.26	0.32	10.0	4	0	white	0
100	6.6	0.21	0.29	1.8	0.026	35.0	128.0	0.99183	3.37	0.48	11.2	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	4.2	0.215	0.23	5.1	0.041	64.0	157.0	0.99688	3.42	0.44	8.0	3	0	white	0	0.944664	0.055336
2	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	0	0.944664	0.055336
3	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	0	0.566946	0.433054
4	4.9	0.335	0.14	1.3	0.036	69.0	168.0	0.99212	3.47	0.46	10.4666666666667	5	0	white	0	0.944664	0.055336
5	5.0	0.74	0.0	1.2	0.041	16.0	46.0	0.99258	4.01	0.59	12.5	6	0	red	0	0.944664	0.055336
6	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	0	0.744681	0.255319
7	5.2	0.3	0.34	1.5	0.038	18.0	96.0	0.98942	3.56	0.48	13.0	8	1	white	0	0.566946	0.433054
8	5.2	0.38	0.26	7.7	0.053	20.0	103.0	0.9925	3.27	0.45	12.2	6	0	white	0	0.863131	0.136869
9	5.4	0.22	0.35	6.5	0.029	26.0	87.0	0.99092	3.29	0.44	12.5	7	1	white	0	0.566946	0.433054
10	5.6	0.13	0.27	4.8	0.028	22.0	104.0	0.9948	3.34	0.45	9.2	6	0	white	0	0.747664	0.252336
11	5.6	0.24	0.34	2.0	0.041	14.0	73.0	0.98981	3.04	0.45	11.6	7	1	white	0	0.566946	0.433054
12	5.6	0.32	0.33	7.4	0.037	25.0	95.0	0.99268	3.25	0.49	11.1	6	0	white	0	0.863131	0.136869
13	5.6	0.35	0.4	6.3	0.022	23.0	174.0	0.9922	3.54	0.5	11.6	7	1	white	0	0.863131	0.136869
14	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	0.944664	0.055336
15	5.7	0.12	0.26	5.5	0.034	21.0	99.0	0.99324	3.09	0.57	9.9	6	0	white	0	0.747664	0.252336
16	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	0.783784	0.216216
17	5.7	0.265	0.28	6.9	0.036	46.0	150.0	0.99299	3.36	0.44	10.8	7	1	white	0	0.863131	0.136869
18	5.7	0.31	0.28	4.1	0.03	22.0	86.0	0.99062	3.31	0.38	11.7	7	1	white	0	0.566946	0.433054
19	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	0.566946	0.433054
20	5.8	0.12	0.21	1.3	0.056	35.0	121.0	0.9908	3.32	0.33	11.4	6	0	white	0	0.744681	0.255319
21	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	0.944664	0.055336
22	5.8	0.15	0.49	1.1	0.048	21.0	98.0	0.9929	3.19	0.48	9.2	5	0	white	0	0.747664	0.252336
23	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	0.944664	0.055336
24	5.8	0.24	0.26	10.05	0.039	63.0	162.0	0.99375	3.33	0.5	11.2	6	0	white	0	0.863131	0.136869
25	5.8	0.27	0.2	14.95	0.044	22.0	179.0	0.9962	3.37	0.37	10.2	5	0	white	0	0.944664	0.055336
26	5.8	0.27	0.4	1.2	0.076	47.0	130.0	0.99185	3.13	0.45	10.3	6	0	white	0	0.883333	0.116667
27	5.8	0.28	0.35	2.3	0.053	36.0	114.0	0.9924	3.28	0.5	10.2	4	0	white	0	0.863131	0.136869
28	5.9	0.27	0.32	2.0	0.034	31.0	102.0	0.98952	3.16	0.56	12.3	6	0	white	0	0.566946	0.433054
29	5.9	0.28	0.39	1.4	0.031	47.0	147.0	0.98836	3.08	0.64	12.9	7	1	white	0	0.566946	0.433054
30	5.9	0.29	0.16	7.9	0.044	48.0	197.0	0.99512	3.21	0.36	9.4	5	0	white	0	0.944664	0.055336
31	5.9	0.32	0.2	14.4	0.05	29.0	144.0	0.99666	3.24	0.41	10.3	6	0	white	0	0.944664	0.055336
32	5.9	0.32	0.39	3.3	0.114	24.0	140.0	0.9934	3.09	0.45	9.2	6	0	white	0	0.863131	0.136869
33	6.0	0.17	0.36	1.7	0.042	14.0	61.0	0.99144	3.22	0.54	10.8	6	0	white	0	0.566946	0.433054
34	6.0	0.19	0.37	9.7	0.032	17.0	50.0	0.9932	3.08	0.66	12.0	6	0	white	0	0.747664	0.252336
35	6.0	0.22	0.28	1.1	0.034	47.0	90.0	0.98862	3.22	0.38	12.6	6	0	white	0	0.566946	0.433054
36	6.0	0.23	0.34	1.3	0.025	23.0	111.0	0.98961	3.36	0.37	12.7	6	0	white	0	0.566946	0.433054
37	6.0	0.27	0.27	1.6	0.046	32.0	113.0	0.9924	3.41	0.51	10.5	7	1	white	0	0.863131	0.136869
38	6.0	0.29	0.25	1.4	0.033	30.0	114.0	0.98794	3.08	0.43	13.2	6	0	white	0	0.744681	0.255319
39	6.0	0.29	0.27	2.3	0.044	20.0	117.0	0.9895	3.31	0.41	12.7	7	1	white	0	0.566946	0.433054
40	6.0	0.29	0.41	10.8	0.048	55.0	149.0	0.9937	3.09	0.59	10.9666666666667	7	1	white	0	0.863131	0.136869
41	6.0	0.29	0.41	10.8	0.048	55.0	149.0	0.9937	3.09	0.59	10.9666666666667	7	1	white	0	0.863131	0.136869
42	6.0	0.31	0.32	7.4	0.175	47.0	159.0	0.9952	3.19	0.5	9.4	6	0	white	0	0.863131	0.136869
43	6.0	0.31	0.47	3.6	0.067	18.0	42.0	0.99549	3.39	0.66	11.0	6	0	red	0	0.863131	0.136869
44	6.0	0.33	0.32	12.9	0.054	6.0	113.0	0.99572	3.3	0.56	11.5	4	0	red	0	0.863131	0.136869
45	6.0	0.35	0.46	0.9	0.033	9.0	65.0	0.98934	3.24	0.35	12.1	4	0	white	0	0.566946	0.433054
46	6.0	0.5	0.0	1.4	0.057	15.0	26.0	0.99448	3.36	0.45	9.5	5	0	red	0	0.944664	0.055336
47	6.0	0.64	0.05	1.9	0.066	9.0	17.0	0.99496	3.52	0.78	10.6	5	0	red	0	0.944664	0.055336
48	6.1	0.16	0.34	6.1	0.034	31.0	114.0	0.99365	3.32	0.58	10.1333333333333	7	1	white	0	0.747664	0.252336
49	6.1	0.2	0.17	1.6	0.048	46.0	129.0	0.991	3.3	0.43	11.4	6	0	white	1	0.47	0.53
50	6.1	0.25	0.48	15.8	0.052	25.0	94.0	0.99782	3.07	0.45	9.2	6	0	white	0	0.863131	0.136869
51	6.1	0.27	0.44	6.7	0.041	61.0	230.0	0.99505	3.12	0.4	8.9	5	0	white	0	0.863131	0.136869
52	6.1	0.31	0.34	2.8	0.042	59.5	162.0	0.99179	3.27	0.47	10.8	6	0	white	0	0.566946	0.433054
53	6.1	0.32	0.25	2.3	0.071	23.0	58.0	0.99633	3.42	0.97	10.6	5	0	red	0	0.944664	0.055336
54	6.1	0.32	0.33	10.7	0.036	27.0	98.0	0.99521	3.34	0.52	10.2	6	0	white	0	0.863131	0.136869
55	6.2	0.18	0.49	4.5	0.047	17.0	90.0	0.9919	3.27	0.37	11.6	6	0	white	0	0.566946	0.433054
56	6.2	0.2	0.26	1.1	0.047	42.0	119.0	0.99158	3.48	0.6	11.0	7	1	white	0	0.566946	0.433054
57	6.2	0.25	0.31	3.2	0.03	32.0	150.0	0.99014	3.18	0.31	12.0	6	0	white	0	0.566946	0.433054
58	6.2	0.25	0.54	7.0	0.046	58.0	176.0	0.99454	3.19	0.7	10.4	5	0	white	0	0.863131	0.136869
59	6.2	0.45	0.73	7.2	0.099	47.0	202.0	0.99582	3.21	0.43	9.2	5	0	white	0	0.863131	0.136869
60	6.2	0.47	0.21	1.0	0.044	13.0	98.0	0.99345	3.14	0.46	9.2	5	0	white	0	0.944664	0.055336
61	6.2	0.55	0.45	12.0	0.049	27.0	186.0	0.9974	3.17	0.5	9.3	6	0	white	0	0.863131	0.136869
62	6.3	0.19	0.29	2.0	0.022	33.0	96.0	0.98902	3.04	0.54	12.8	7	1	white	0	0.566946	0.433054
63	6.3	0.19	0.33	10.1	0.063	63.0	133.0	0.99561	2.86	0.41	9.1	5	0	white	0	0.747664	0.252336
64	6.3	0.2	0.26	4.7	0.04	108.0	168.0	0.99278	3.07	0.75	10.7	7	1	white	0	0.747664	0.252336
65	6.3	0.21	0.58	10.0	0.081	34.0	126.0	0.9962	2.95	0.46	8.9	5	0	white	0	0.747664	0.252336
66	6.3	0.24	0.29	13.7	0.035	53.0	134.0	0.99567	3.17	0.38	10.6	6	0	white	0	0.863131	0.136869
67	6.3	0.24	0.35	2.3	0.039	43.0	109.0	0.99056	3.34	0.44	11.8	6	0	white	0	0.566946	0.433054
68	6.3	0.25	0.22	3.3	0.048	41.0	161.0	0.99256	3.16	0.5	10.5	6	0	white	0	0.944664	0.055336
69	6.3	0.27	0.38	0.9	0.051	7.0	140.0	0.9926	3.45	0.5	10.5	7	1	white	0	0.863131	0.136869
70	6.3	0.27	0.51	7.6	0.049	35.0	200.0	0.99548	3.16	0.54	9.4	4	0	white	0	0.863131	0.136869
71	6.3	0.27	0.51	7.6	0.049	35.0	200.0	0.99548	3.16	0.54	9.4	4	0	white	0	0.863131	0.136869
72	6.3	0.3	0.48	7.4	0.053	34.0	149.0	0.99472	3.18	0.53	9.8	5	0	white	0	0.863131	0.136869
73	6.3	0.32	0.17	17.75	0.06	51.0	190.0	0.99916	3.13	0.48	8.8	6	0	white	0	0.944664	0.055336
74	6.3	0.32	0.32	1.5	0.03	24.0	101.0	0.98923	3.21	0.42	13.0	5	0	white	0	0.566946	0.433054
75	6.3	0.34	0.33	4.6	0.034	19.0	80.0	0.9917	3.38	0.58	12.0	7	1	white	0	0.566946	0.433054
76	6.3	0.4	0.24	5.1	0.036	43.0	131.0	0.99186	3.24	0.44	11.3	6	0	white	0	0.944664	0.055336
77	6.3	0.6	0.44	11.0	0.05	50.0	245.0	0.9972	3.19	0.57	9.3	4	0	white	0	0.863131	0.136869
78	6.4	0.105	0.29	1.1	0.035	44.0	140.0	0.99142	3.17	0.55	10.7	7	1	white	0	0.566946	0.433054
79	6.4	0.125	0.29	5.85	0.042	24.0	99.0	0.992	3.23	0.32	12.0	7	1	white	0	0.566946	0.433054
80	6.4	0.17	0.32	2.4	0.048	41.0	200.0	0.9938	3.5	0.5	9.7	6	0	white	0	0.747664	0.252336
81	6.4	0.23	0.27	2.1	0.042	35.0	100.0	0.99094	3.03	0.63	10.9	6	0	white	0	0.566946	0.433054
82	6.4	0.24	0.26	8.2	0.054	47.0	182.0	0.99538	3.12	0.5	9.5	5	0	white	0	0.863131	0.136869
83	6.4	0.24	0.49	5.8	0.053	25.0	120.0	0.9942	3.01	0.98	10.5	6	0	white	0	0.863131	0.136869
84	6.4	0.24	0.49	5.8	0.053	25.0	120.0	0.9942	3.01	0.98	10.5	6	0	white	0	0.863131	0.136869
85	6.4	0.25	0.32	11.3	0.038	69.0	192.0	0.99573	3.14	0.5	10.2	6	0	white	0	0.863131	0.136869
86	6.4	0.27	0.49	7.3	0.046	53.0	206.0	0.9956	3.24	0.43	9.2	6	0	white	0	0.863131	0.136869
87	6.4	0.29	0.18	15.0	0.04	21.0	116.0	0.99736	3.14	0.5	9.2	5	0	white	0	0.944664	0.055336
88	6.4	0.33	0.44	8.9	0.055	52.0	164.0	0.99488	3.1	0.48	9.6	5	0	white	0	0.863131	0.136869
89	6.4	0.38	0.24	7.2	0.047	41.0	151.0	0.99604	3.11	0.6	9.2	5	0	white	0	0.944664	0.055336
90	6.4	0.38	0.24	7.2	0.047	41.0	151.0	0.99604	3.11	0.6	9.2	5	0	white	0	0.944664	0.055336
91	6.4	0.5	0.2	2.4	0.059	19.0	112.0	0.99314	3.18	0.4	9.2	6	0	white	0	0.944664	0.055336
92	6.5	0.16	0.33	4.8	0.043	45.0	114.0	0.992	3.18	0.44	11.2	6	0	white	0	0.566946	0.433054
93	6.5	0.18	0.34	1.6	0.04	43.0	148.0	0.9912	3.32	0.59	11.5	8	1	white	0	0.566946	0.433054
94	6.5	0.22	0.32	2.2	0.028	36.0	92.0	0.99076	3.27	0.59	11.9	7	1	white	0	0.566946	0.433054
95	6.5	0.23	0.2	7.5	0.05	44.0	179.0	0.99504	3.18	0.48	9.53333333333333	5	0	white	0	0.944664	0.055336
96	6.5	0.24	0.38	1.0	0.027	31.0	90.0	0.98926	3.24	0.36	12.3	6	0	white	0	0.566946	0.433054
97	6.5	0.28	0.34	4.6	0.054	22.0	130.0	0.99193	3.2	0.46	12.0	7	1	white	0	0.883333	0.116667
98	6.5	0.34	0.28	1.8	0.041	43.0	188.0	0.9928	3.13	0.37	9.6	6	0	white	0	0.863131	0.136869
99	6.5	0.35	0.28	12.4	0.051	86.0	213.0	0.9962	3.16	0.51	9.9	6	0	white	0	0.863131	0.136869
100	6.5	0.37	0.33	3.5	0.036	23.0	92.0	0.99136	3.18	0.38	11.2	6	0	white	0	0.566946	0.433054

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([[1043,    7],
       [ 245,    8]])

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="\\"citric_acid\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n0 -> 1 [label="<= 0.259375", color="#000000", fontcolor="#000000"]\n0 -> 2 [label="> 0.259375", color="#000000", fontcolor="#000000"]\n1 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n1 -> 3 [label="<= 0.991474", color="#000000", fontcolor="#000000"]\n1 -> 4 [label="> 0.991474", color="#000000", fontcolor="#000000"]\n2 [label="\\"density\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n2 -> 5 [label="<= 0.992198", color="#000000", fontcolor="#000000"]\n2 -> 6 [label="> 0.992198", color="#000000", fontcolor="#000000"]\n3 [label="\\"residual_sugar\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n3 -> 7 [label="<= 1.56875", color="#000000", fontcolor="#000000"]\n3 -> 8 [label="> 1.56875", color="#000000", fontcolor="#000000"]\n4 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n4 -> 9 [label="<= 0.031688", color="#000000", fontcolor="#000000"]\n4 -> 10 [label="> 0.031688", color="#000000", fontcolor="#000000"]\n5 [label="\\"chlorides\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n5 -> 11 [label="<= 0.050375", color="#000000", fontcolor="#000000"]\n5 -> 12 [label="> 0.050375", color="#000000", fontcolor="#000000"]\n6 [label="\\"volatile_acidity\\"", shape="box", style="filled", fillcolor="#FFFFFFDD", fontcolor="#000000", color="#000000"]\n6 -> 13 [label="<= 0.220625", color="#000000", fontcolor="#000000"]\n6 -> 14 [label="> 0.220625", 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): 0.74</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"]\n8 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#efc5b5" color="#000000"><FONT color="#000000"><b>prediction: 1 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">prob(0): 0.47</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"]\n9 [label=<<table border="0" cellspacing="0"> <tr><td port="port1" border="1" bgcolor="#87cefa" color="#000000"><FONT color="#000000"><b>prediction: 0 </b></FONT></td></tr><tr><td port="port0" border="1" align="left" color="#000000"><FONT color="#000000">prob(0): 0.78</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.22</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): 0.94</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.06</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): 0.57</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.43</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): 0.88</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.12</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): 0.75</FONT></td></tr><tr><td port="port1" border="1" align="left" color="#000000"><FONT color="#000000">prob(1): 0.25</FONT></td></tr></table>>, fillcolor="#FFFFFFDD", fontcolor="#000000", shape="none", color="#000000"]\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]: 
{'max_features': 'auto',
 'max_leaf_nodes': 32,
 'max_depth': 3,
 'min_samples_leaf': 5,
 'min_info_gain': 0.0,
 'nbins': 32}

And to manually change some of the parameters:

model.set_params({'max_depth': 5})

Model Register

In order to register the model for tracking and versioning:

model.register("model_v1")

Please refer to /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 "citric_acid" < 0.259375 THEN (CASE WHEN "density" < 0.991474 THEN (CASE WHEN "residual_sugar" < 1.56875 THEN 0 ELSE 1 END) ELSE (CASE WHEN "chlorides" < 0.031688 THEN 0 ELSE 0 END) END) ELSE (CASE WHEN "density" < 0.992198 THEN (CASE WHEN "chlorides" < 0.050375 THEN 0 ELSE 0 END) ELSE (CASE WHEN "volatile_acidity" < 0.220625 THEN 0 ELSE 0 END) END) END)'

To Python

To obtain the prediction function in Python syntax, use the following code:

X = [[4.2, 0.17, 0.36, 1.8, 0.029, 0.9899]]

model.to_python()(X)
Out[10]: array([0])

Hint

The to_python() method is used to retrieve predictions, probabilities, or cluster distances. For specific details on how to use this method for different model types, refer to the relevant documentation for each model.

__init__(name: str = None, overwrite_model: bool = False, max_features: Literal['auto', 'max'] | int = 'auto', max_leaf_nodes: Annotated[int | float | Decimal, 'Python Numbers'] = 1000000000.0, max_depth: int = 100, min_samples_leaf: int = 1, min_info_gain: Annotated[int | float | Decimal, 'Python Numbers'] = 0.0, nbins: int = 32) → None

Must be overridden in the child class

Methods

__init__([name, overwrite_model, ...])	Must be overridden in the child class
classification_report([metrics, cutoff, ...])	Computes a classification report using multiple model evaluation metrics (auc, accuracy, f1...).
confusion_matrix([pos_label, cutoff])	Computes the model confusion matrix.
contour([pos_label, nbins, chart])	Draws the model's contour plot.
cutoff_curve([pos_label, nbins, show, chart])	Draws the model Cutoff curve.
deploySQL([X, pos_label, cutoff, allSQL])	Returns the SQL code needed to deploy the model.
does_model_exists(name[, raise_error, ...])	Checks whether the model is stored in the Vertica database.
drop()	Drops the model from the Vertica database.
export_models(name, path[, kind])	Exports machine learning models.
features_importance([tree_id, show, chart])	Computes the model's features importance.
fit(input_relation, X, y[, test_relation, ...])	Trains the model.
get_attributes([attr_name])	Returns the model attributes.
get_match_index(x, col_list[, str_check])	Returns the matching index.
get_params()	Returns the parameters of the model.
get_plotting_lib([class_name, chart, ...])	Returns the first available library (Plotly, Matplotlib, or Highcharts) to draw a specific graphic.
get_score([tree_id])	Returns the feature importance metrics for the input tree.
get_tree([tree_id])	Returns a table with all the input tree information.
get_vertica_attributes([attr_name])	Returns the model Vertica attributes.
import_models(path[, schema, kind])	Imports machine learning models.
lift_chart([pos_label, nbins, show, chart])	Draws the model Lift Chart.
plot([max_nb_points, chart])	Draws the model.
plot_tree([tree_id, pic_path])	Draws the input tree.
prc_curve([pos_label, nbins, show, chart])	Draws the model PRC curve.
predict(vdf[, X, name, cutoff, inplace])	Predicts using the input relation.
predict_proba(vdf[, X, name, pos_label, inplace])	Returns the model's probabilities using the input relation.
register(registered_name[, raise_error])	Registers the model and adds it to in-DB Model versioning environment with a status of 'under_review'.
report([metrics, cutoff, labels, nbins])	Computes a classification report using multiple model evaluation metrics (auc, accuracy, f1...).
roc_curve([pos_label, nbins, show, chart])	Draws the model ROC curve.
score([metric, average, pos_label, cutoff, ...])	Computes the model score.
set_params([parameters])	Sets the parameters of the model.
summarize()	Summarizes the model.
to_binary(path)	Exports the model to the Vertica Binary format.
to_graphviz([tree_id, classes_color, ...])	Returns the code for a Graphviz tree.
to_memmodel()	Converts the model to an InMemory object that can be used for different types of predictions.
to_pmml(path)	Exports the model to PMML.
to_python([return_proba, ...])	Returns the Python function needed for in-memory scoring without using built-in Vertica functions.
to_sql([X, return_proba, ...])	Returns the SQL code needed to deploy the model without using built-in Vertica functions.
to_tf(path)	Exports the model to the Frozen Graph format (TensorFlow).

Attributes

object_type