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verticapy.machine_learning.vertica.naive_bayes.NaiveBayes

class verticapy.machine_learning.vertica.naive_bayes.NaiveBayes(name: str = None, overwrite_model: bool = False, alpha: int | float | Decimal = 1.0, nbtype: Literal['auto', 'bernoulli', 'categorical', 'multinomial', 'gaussian'] = 'auto')

Creates a NaiveBayes object using the Vertica Naive Bayes algorithm. It is a “probabilistic classifier” based on applying Bayes’ theorem with strong (naïve) independence assumptions between the features.

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.

alpha: float, optional

A float that specifies use of Laplace smoothing if the event model is categorical, multinomial, or Bernoulli.

nbtype: str, optional

Naive Bayes type.

• auto:

  Vertica NaiveBayes objects treat columns according to data type:

  • FLOAT:

    values are assumed to follow some Gaussian distribution.

  • INTEGER:

    values are assumed to belong to one multinomial distribution.

  • CHAR/VARCHAR:

    values are assumed to follow some categorical distribution. The string values stored in these columns must be no greater than 128 characters.

  • BOOLEAN:

    values are treated as categorical with two values.

• bernoulli:

  Casts the variables to boolean.

• categorical:

  Casts the variables to categorical.

• multinomial:

  Casts the variables to integer.

• gaussian:

  Casts the variables to float.

Attributes

Many attributes are created during the fitting phase.

prior_: numpy.array

The model’s classes probabilities.

attributes: list of dict

list of the model’s attributes. Each feature is represented by a dictionary, which differs based on the distribution.

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.

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 iris dataset.

import verticapy.datasets as vpd

data = vpd.load_iris()

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)
1	3.3	4.5	5.6	7.8	Iris-setosa
2	3.3	4.5	5.6	7.8	Iris-setosa
3	3.3	4.5	5.6	7.8	Iris-setosa
4	3.3	4.5	5.6	7.8	Iris-setosa
5	3.3	4.5	5.6	7.8	Iris-setosa
6	3.3	4.5	5.6	7.8	Iris-setosa
7	3.3	4.5	5.6	7.8	Iris-setosa
8	3.3	4.5	5.6	7.8	Iris-setosa
9	3.3	4.5	5.6	7.8	Iris-setosa
10	3.3	4.5	5.6	7.8	Iris-setosa
11	3.3	4.5	5.6	7.8	Iris-setosa
12	3.3	4.5	5.6	7.8	Iris-setosa
13	3.3	4.5	5.6	7.8	Iris-setosa
14	3.3	4.5	5.6	7.8	Iris-setosa
15	3.3	4.5	5.6	7.8	Iris-setosa
16	3.3	4.5	5.6	7.8	Iris-setosa
17	3.3	4.5	5.6	7.8	Iris-setosa
18	3.3	4.5	5.6	7.8	Iris-setosa
19	3.3	4.5	5.6	7.8	Iris-setosa
20	3.3	4.5	5.6	7.8	Iris-setosa
21	3.3	4.5	5.6	7.8	Iris-setosa
22	3.3	4.5	5.6	7.8	Iris-setosa
23	3.3	4.5	5.6	7.8	Iris-setosa
24	3.3	4.5	5.6	7.8	Iris-setosa
25	3.3	4.5	5.6	7.8	Iris-setosa
26	3.3	4.5	5.6	7.8	Iris-setosa
27	3.3	4.5	5.6	7.8	Iris-setosa
28	3.3	4.5	5.6	7.8	Iris-setosa
29	3.3	4.5	5.6	7.8	Iris-setosa
30	3.3	4.5	5.6	7.8	Iris-setosa
31	3.3	4.5	5.6	7.8	Iris-setosa
32	3.3	4.5	5.6	7.8	Iris-setosa
33	3.3	4.5	5.6	7.8	Iris-setosa
34	3.3	4.5	5.6	7.8	Iris-setosa
35	3.3	4.5	5.6	7.8	Iris-setosa
36	3.3	4.5	5.6	7.8	Iris-setosa
37	3.3	4.5	5.6	7.8	Iris-setosa
38	3.3	4.5	5.6	7.8	Iris-setosa
39	3.3	4.5	5.6	7.8	Iris-setosa
40	3.3	4.5	5.6	7.8	Iris-setosa
41	3.3	4.5	5.6	7.8	Iris-setosa
42	3.3	4.5	5.6	7.8	Iris-setosa
43	4.3	3.0	1.1	0.1	Iris-setosa
44	4.3	4.7	9.6	1.8	Iris-virginica
45	4.3	4.7	9.6	1.8	Iris-virginica
46	4.3	4.7	9.6	1.8	Iris-virginica
47	4.3	4.7	9.6	1.8	Iris-virginica
48	4.3	4.7	9.6	1.8	Iris-virginica
49	4.3	4.7	9.6	1.8	Iris-virginica
50	4.3	4.7	9.6	1.8	Iris-virginica
51	4.3	4.7	9.6	1.8	Iris-virginica
52	4.3	4.7	9.6	1.8	Iris-virginica
53	4.3	4.7	9.6	1.8	Iris-virginica
54	4.3	4.7	9.6	1.8	Iris-virginica
55	4.3	4.7	9.6	1.8	Iris-virginica
56	4.3	4.7	9.6	1.8	Iris-virginica
57	4.3	4.7	9.6	1.8	Iris-virginica
58	4.3	4.7	9.6	1.8	Iris-virginica
59	4.3	4.7	9.6	1.8	Iris-virginica
60	4.3	4.7	9.6	1.8	Iris-virginica
61	4.3	4.7	9.6	1.8	Iris-virginica
62	4.3	4.7	9.6	1.8	Iris-virginica
63	4.3	4.7	9.6	1.8	Iris-virginica
64	4.3	4.7	9.6	1.8	Iris-virginica
65	4.3	4.7	9.6	1.8	Iris-virginica
66	4.3	4.7	9.6	1.8	Iris-virginica
67	4.3	4.7	9.6	1.8	Iris-virginica
68	4.3	4.7	9.6	1.8	Iris-virginica
69	4.3	4.7	9.6	1.8	Iris-virginica
70	4.3	4.7	9.6	1.8	Iris-virginica
71	4.3	4.7	9.6	1.8	Iris-virginica
72	4.3	4.7	9.6	1.8	Iris-virginica
73	4.3	4.7	9.6	1.8	Iris-virginica
74	4.3	4.7	9.6	1.8	Iris-virginica
75	4.3	4.7	9.6	1.8	Iris-virginica
76	4.3	4.7	9.6	1.8	Iris-virginica
77	4.3	4.7	9.6	1.8	Iris-virginica
78	4.3	4.7	9.6	1.8	Iris-virginica
79	4.3	4.7	9.6	1.8	Iris-virginica
80	4.3	4.7	9.6	1.8	Iris-virginica
81	4.3	4.7	9.6	1.8	Iris-virginica
82	4.3	4.7	9.6	1.8	Iris-virginica
83	4.3	4.7	9.6	1.8	Iris-virginica
84	4.3	4.7	9.6	1.8	Iris-virginica
85	4.3	4.7	9.6	1.8	Iris-virginica
86	4.4	2.9	1.4	0.2	Iris-setosa
87	4.4	3.0	1.3	0.2	Iris-setosa
88	4.4	3.2	1.3	0.2	Iris-setosa
89	4.5	2.3	1.3	0.3	Iris-setosa
90	4.6	3.1	1.5	0.2	Iris-setosa
91	4.6	3.2	1.4	0.2	Iris-setosa
92	4.6	3.4	1.4	0.3	Iris-setosa
93	4.6	3.6	1.0	0.2	Iris-setosa
94	4.7	3.2	1.3	0.2	Iris-setosa
95	4.7	3.2	1.6	0.2	Iris-setosa
96	4.8	3.0	1.4	0.1	Iris-setosa
97	4.8	3.0	1.4	0.3	Iris-setosa
98	4.8	3.1	1.6	0.2	Iris-setosa
99	4.8	3.4	1.6	0.2	Iris-setosa
100	4.8	3.4	1.9	0.2	Iris-setosa

Rows: 1-100 | Columns: 5

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_iris()
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 NaiveBayes model:

from verticapy.machine_learning.vertica import NaiveBayes

Then we can create the model:

model = NaiveBayes()

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,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "Species",
    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.

Metrics

We can get the entire report using:

model.report()

	Iris-setosa	Iris-versicolor	Iris-virginica	avg_macro	avg_weighted	avg_micro
auc	1.0	0.9806201550387597	0.9916666666666667	0.9907622739018088	0.9935453251067868	[null]
prc_auc	1.0	0.899074074074074	0.9911651165863123	0.9634130635534621	0.9833144335207336	[null]
accuracy	1.0	0.8979591836734694	0.8979591836734694	0.9319727891156463	0.9375260308204915	0.9319727891156463
log_loss	0.00258057060444779	0.0779784350604565	0.0808958638264282	0.053818289830444156	0.05017147313635763	[null]
precision	1.0	0.5555555555555556	0.9523809523809523	0.8359788359788359	0.9222546161321672	0.8979591836734694
recall	1.0	0.8333333333333334	0.8333333333333334	0.888888888888889	0.8979591836734694	0.8979591836734694
f1_score	1.0	0.6666666666666667	0.888888888888889	0.851851851851852	0.9047619047619048	0.8979591836734694
mcc	1.0	0.6267181379708672	0.8013876853447538	0.8093686077718737	0.8570125158387611	0.8469387755102041
informedness	1.0	0.7403100775193798	0.7933333333333334	0.8445478036175711	0.8669767441860466	0.8469387755102042
markedness	1.0	0.5305555555555554	0.8095238095238093	0.780026455026455	0.8492225461613215	0.8469387755102042
csi	1.0	0.5	0.8	0.7666666666666666	0.8408163265306123	0.8148148148148148

Rows: 1-11 | Columns: 7

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)

	Iris-setosa	Iris-versicolor	Iris-virginica	avg_macro	avg_weighted	avg_micro
auc	1.0	0.9806201550387597	0.9916666666666667	0.9907622739018088	0.9935453251067868	[null]
prc_auc	1.0	0.899074074074074	0.9911651165863123	0.9634130635534621	0.9833144335207336	[null]
accuracy	1.0	0.9183673469387755	0.9387755102040817	0.9523809523809524	0.9600166597251146	0.9523809523809523
log_loss	0.00258057060444779	0.0779784350604565	0.0808958638264282	0.053818289830444156	0.05017147313635763	[null]
precision	1.0	0.6	0.9565217391304348	0.8521739130434783	0.9297249334516416	0.9038461538461539
recall	1.0	1.0	0.9166666666666666	0.9722222222222222	0.9591836734693877	0.9591836734693877
f1_score	1.0	0.7499999999999999	0.9361702127659574	0.8953900709219859	0.9381241858445505	0.9306930693069307
mcc	1.0	0.737689668161096	0.8781314407318439	0.87194036963098	0.9081896446230782	0.8953885457014064
informedness	1.0	0.9069767441860463	0.8766666666666665	0.9278811369509042	0.9282012339819647	0.9081632653061225
markedness	1.0	0.6000000000000001	0.8795986622073579	0.8265328874024527	0.8920483243464611	0.8827935222672065
csi	1.0	0.6	0.88	0.8266666666666667	0.8922448979591836	0.8703703703703703

Rows: 1-11 | Columns: 7

You can also use the NaiveBayes.score function to compute any classification metric. The default metric is the accuracy:

model.score(metric = "f1", average = "macro")
Out[4]: 0.851851851851852

Note

For multi-class scoring, verticapy allows the flexibility to use three averaging techniques: micro, macro and weighted. Please refer to this link for more details on how they are calculated.

Prediction

Prediction is straight-forward:

model.predict(
    test,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "prediction",
)

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)	Abc prediction Varchar(100)
1	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
2	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
3	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
4	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
5	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
6	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
7	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
8	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
10	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
11	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
12	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
13	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
14	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
15	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
16	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
17	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
18	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica
19	4.6	3.4	1.4	0.3	Iris-setosa	Iris-setosa
20	4.6	3.6	1.0	0.2	Iris-setosa	Iris-setosa
21	4.8	3.1	1.6	0.2	Iris-setosa	Iris-setosa
22	4.9	2.5	4.5	1.7	Iris-virginica	Iris-versicolor
23	5.0	3.3	1.4	0.2	Iris-setosa	Iris-setosa
24	5.1	3.5	1.4	0.2	Iris-setosa	Iris-setosa
25	5.4	3.7	1.5	0.2	Iris-setosa	Iris-setosa
26	5.4	3.9	1.3	0.4	Iris-setosa	Iris-setosa
27	5.4	3.9	1.7	0.4	Iris-setosa	Iris-setosa
28	5.5	3.5	1.3	0.2	Iris-setosa	Iris-setosa
29	5.7	3.0	4.2	1.2	Iris-versicolor	Iris-versicolor
30	5.9	3.2	4.8	1.8	Iris-versicolor	Iris-virginica
31	6.0	2.7	5.1	1.6	Iris-versicolor	Iris-versicolor
32	6.0	3.0	4.8	1.8	Iris-virginica	Iris-versicolor
33	6.1	2.8	4.0	1.3	Iris-versicolor	Iris-versicolor
34	6.1	2.9	4.7	1.4	Iris-versicolor	Iris-versicolor
35	6.2	2.9	4.3	1.3	Iris-versicolor	Iris-versicolor
36	6.3	2.5	5.0	1.9	Iris-virginica	Iris-virginica
37	6.3	2.7	4.9	1.8	Iris-virginica	Iris-versicolor
38	6.3	2.8	5.1	1.5	Iris-virginica	Iris-versicolor
39	6.3	2.9	5.6	1.8	Iris-virginica	Iris-virginica
40	6.4	2.8	5.6	2.1	Iris-virginica	Iris-virginica
41	6.7	3.0	5.2	2.3	Iris-virginica	Iris-virginica
42	6.7	3.1	5.6	2.4	Iris-virginica	Iris-virginica
43	6.7	3.3	5.7	2.5	Iris-virginica	Iris-virginica
44	6.9	3.1	5.4	2.1	Iris-virginica	Iris-virginica
45	7.2	3.6	6.1	2.5	Iris-virginica	Iris-virginica
46	7.7	2.6	6.9	2.3	Iris-virginica	Iris-virginica
47	7.7	3.0	6.1	2.3	Iris-virginica	Iris-virginica
48	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa
49	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica

Rows: 1-49 | Columns: 6

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,
    [
        "SepalLengthCm",
        "SepalWidthCm",
        "PetalLengthCm",
        "PetalWidthCm",
    ],
    "prediction",
)

	123 SepalLengthCm Numeric(7)	123 SepalWidthCm Numeric(7)	123 PetalLengthCm Numeric(7)	123 PetalWidthCm Numeric(7)	Abc Species Varchar(30)	Abc prediction Varchar(100)	Abc prediction_irissetosa Varchar(100)	Abc prediction_irisversicolor Varchar(100)	Abc prediction_irisvirginica Varchar(100)
1	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
2	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
3	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
4	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
5	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
6	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
7	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
8	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
9	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
10	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
11	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
12	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
13	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
14	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
15	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
16	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
17	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
18	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767
19	4.6	3.4	1.4	0.3	Iris-setosa	Iris-setosa	1	5.90302e-14	2.07352e-14
20	4.6	3.6	1.0	0.2	Iris-setosa	Iris-setosa	1	3.40448e-18	2.60895e-16
21	4.8	3.1	1.6	0.2	Iris-setosa	Iris-setosa	1	6.58419e-13	8.9424e-16
22	4.9	2.5	4.5	1.7	Iris-virginica	Iris-versicolor	0.015503	0.803665	0.180832
23	5.0	3.3	1.4	0.2	Iris-setosa	Iris-setosa	1	4.30877e-14	7.72366e-16
24	5.1	3.5	1.4	0.2	Iris-setosa	Iris-setosa	1	1.44006e-14	7.63312e-16
25	5.4	3.7	1.5	0.2	Iris-setosa	Iris-setosa	1	2.42326e-14	1.19441e-15
26	5.4	3.9	1.3	0.4	Iris-setosa	Iris-setosa	1	6.09786e-14	1.63566e-12
27	5.4	3.9	1.7	0.4	Iris-setosa	Iris-setosa	1	7.05111e-12	2.44434e-12
28	5.5	3.5	1.3	0.2	Iris-setosa	Iris-setosa	1	1.68971e-14	1.2626e-15
29	5.7	3.0	4.2	1.2	Iris-versicolor	Iris-versicolor	0.000640977	0.999313	4.62317e-05
30	5.9	3.2	4.8	1.8	Iris-versicolor	Iris-virginica	0.0167434	0.334584	0.648672
31	6.0	2.7	5.1	1.6	Iris-versicolor	Iris-versicolor	0.00120372	0.940276	0.0585206
32	6.0	3.0	4.8	1.8	Iris-virginica	Iris-versicolor	0.0075102	0.551957	0.440533
33	6.1	2.8	4.0	1.3	Iris-versicolor	Iris-versicolor	0.000114008	0.999801	8.52623e-05
34	6.1	2.9	4.7	1.4	Iris-versicolor	Iris-versicolor	0.000239158	0.998651	0.0011096
35	6.2	2.9	4.3	1.3	Iris-versicolor	Iris-versicolor	0.000118777	0.999754	0.000126845
36	6.3	2.5	5.0	1.9	Iris-virginica	Iris-virginica	0.00253309	0.187117	0.81035
37	6.3	2.7	4.9	1.8	Iris-virginica	Iris-versicolor	0.00209139	0.61447	0.383439
38	6.3	2.8	5.1	1.5	Iris-virginica	Iris-versicolor	0.000512624	0.98203	0.0174578
39	6.3	2.9	5.6	1.8	Iris-virginica	Iris-virginica	0.00358159	0.0281004	0.968318
40	6.4	2.8	5.6	2.1	Iris-virginica	Iris-virginica	0.0037708	0.000259601	0.99597
41	6.7	3.0	5.2	2.3	Iris-virginica	Iris-virginica	0.0152803	3.43483e-05	0.984685
42	6.7	3.1	5.6	2.4	Iris-virginica	Iris-virginica	0.0330208	3.76687e-07	0.966979
43	6.7	3.3	5.7	2.5	Iris-virginica	Iris-virginica	0.12142	1.01411e-08	0.87858
44	6.9	3.1	5.4	2.1	Iris-virginica	Iris-virginica	0.00211698	0.000163728	0.997719
45	7.2	3.6	6.1	2.5	Iris-virginica	Iris-virginica	0.0313574	1.59568e-11	0.968643
46	7.7	2.6	6.9	2.3	Iris-virginica	Iris-virginica	0.000110741	1.41021e-12	0.999889
47	7.7	3.0	6.1	2.3	Iris-virginica	Iris-virginica	0.000441386	3.17972e-09	0.999559
48	3.3	4.5	5.6	7.8	Iris-setosa	Iris-setosa	1	5.32765e-259	3.04755e-177
49	4.3	4.7	9.6	1.8	Iris-virginica	Iris-virginica	0.00232998	1.9755e-38	0.99767

Rows: 1-49 | Columns: 9

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.

model.confusion_matrix()
Out[5]: 
array([[19,  0,  0],
       [ 0,  5,  1],
       [ 0,  4, 20]])

Hint

In the context of multi-class classification, you typically work with an overall confusion matrix that summarizes the classification efficiency across all classes. However, you have the flexibility to specify a pos_label and adjust the cutoff threshold. In this case, a binary confusion matrix is computed, where the chosen class is treated as the positive class, allowing you to evaluate its efficiency as if it were a binary classification problem.

model.confusion_matrix(pos_label = "Iris-setosa", cutoff = 0.6)
Out[6]: 
array([[30,  0],
       [ 0, 19]])

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(pos_label = "Iris-setosa")

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

Contour plot is another useful plot that can be produced for models with two predictors.

model.contour(pos_label = "Iris-setosa")

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[7]: {'alpha': 1.0, 'nbtype': 'auto'}

And to manually change some of the parameters:

model.set_params({'alpha': 0.9})

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[9]: 'CASE WHEN "SepalLengthCm" IS NULL OR "SepalWidthCm" IS NULL OR "PetalLengthCm" IS NULL OR "PetalWidthCm" IS NULL THEN NULL WHEN 0.3244726886220993 * EXP(- POWER("SepalLengthCm" - 5.41486486486486, 2) / 3.02338763420952) * 0.4491388607461844 * EXP(- POWER("SepalWidthCm" - 3.87027027027027, 2) / 1.577934098482068) * 0.19437108725076185 * EXP(- POWER("PetalLengthCm" - 7.6472972972973, 2) / 8.42532765642342) * 1.9328615063345225 * EXP(- POWER("PetalWidthCm" - 1.90135135135135, 2) / 0.085201777119588) * 0.375634517766497 >= 0.4505140594417424 * EXP(- POWER("SepalLengthCm" - 4.17594936708861, 2) / 1.568315481986386) * 0.6362690709840972 * EXP(- POWER("SepalWidthCm" - 3.92405063291139, 2) / 0.786264199935092) * 0.1921613470484011 * EXP(- POWER("PetalLengthCm" - 3.46075949367089, 2) / 8.6202142161636) * 0.10494293818212533 * EXP(- POWER("PetalWidthCm" - 3.87721518987342, 2) / 28.9030509574812) * 0.401015228426396 AND 0.3244726886220993 * EXP(- POWER("SepalLengthCm" - 5.41486486486486, 2) / 3.02338763420952) * 0.4491388607461844 * EXP(- POWER("SepalWidthCm" - 3.87027027027027, 2) / 1.577934098482068) * 0.19437108725076185 * EXP(- POWER("PetalLengthCm" - 7.6472972972973, 2) / 8.42532765642342) * 1.9328615063345225 * EXP(- POWER("PetalWidthCm" - 1.90135135135135, 2) / 0.085201777119588) * 0.375634517766497 >= 0.7292913131474902 * EXP(- POWER("SepalLengthCm" - 5.92727272727273, 2) / 0.598477801268546) * 1.2291225672892683 * EXP(- POWER("SepalWidthCm" - 2.75, 2) / 0.210697674418604) * 0.8486436275756416 * EXP(- POWER("PetalLengthCm" - 4.225, 2) / 0.441976744186038) * 2.0783710790945586 * EXP(- POWER("PetalWidthCm" - 1.31136363636364, 2) / 0.073689217758985) * 0.223350253807107 THEN \'Iris-virginica\' WHEN 0.7292913131474902 * EXP(- POWER("SepalLengthCm" - 5.92727272727273, 2) / 0.598477801268546) * 1.2291225672892683 * EXP(- POWER("SepalWidthCm" - 2.75, 2) / 0.210697674418604) * 0.8486436275756416 * EXP(- POWER("PetalLengthCm" - 4.225, 2) / 0.441976744186038) * 2.0783710790945586 * EXP(- POWER("PetalWidthCm" - 1.31136363636364, 2) / 0.073689217758985) * 0.223350253807107 >= 0.4505140594417424 * EXP(- POWER("SepalLengthCm" - 4.17594936708861, 2) / 1.568315481986386) * 0.6362690709840972 * EXP(- POWER("SepalWidthCm" - 3.92405063291139, 2) / 0.786264199935092) * 0.1921613470484011 * EXP(- POWER("PetalLengthCm" - 3.46075949367089, 2) / 8.6202142161636) * 0.10494293818212533 * EXP(- POWER("PetalWidthCm" - 3.87721518987342, 2) / 28.9030509574812) * 0.401015228426396 THEN \'Iris-versicolor\' ELSE \'Iris-setosa\' END'

To Python

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

X = [[5, 2, 3, 1]]

model.to_python()(X)
Out[11]: array(['Iris-versicolor'], dtype='<U15')

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, alpha: int | float | Decimal = 1.0, nbtype: Literal['auto', 'bernoulli', 'categorical', 'multinomial', 'gaussian'] = 'auto') → None

Must be overridden in the child class

Methods

__init__([name, overwrite_model, alpha, nbtype])	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.
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_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.
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_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