The categorical Naive Bayes test is located under Machine Learning ( ) in Classification, on the left task pane. Alternatively, use the search bar for finding the Categorical Naive Bayes test feature. Use the drag-and-drop method or double-click to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis.
The available properties of the Categorical Naive Bayes test are shown below.
The table below describes the different properties of the Categorical Naive Bayes Test.
Field | Description | Remark | |
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Run | It allows you to run the node | - | |
Explore | It allows you to explore the successfully executed node. | - | |
Vertical Ellipses | The available options are
| - | |
Task Name | It is the name of the task selected on the workbook canvas. |
| |
Dependent Variable | It allows you to select the categorical (discrete) variable. | Any one of the available binary variables can be selected. | |
Independent Variables | It allows you to select discrete features that are categorically distributed variables and numerical variables. |
| |
Advanced | Class Prior | It is the prior probabilities of the classes. |
|
Alpha | It allows you to enter the alpha value or a significance level |
| |
Fit Prior | It allows you to consider the data partially that fits in the memory. | The default is True. | |
Minimum Categories | It is the categories per feature. |
| |
Dimensionality Reduction | It allows you to select the dimensionality reduction technique. |
| |
Add result as variable |
|
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Node Configuration | It allows you to select the instance of the AWS server to provide control over the execution of a task in a workbook or workflow. | For more details, refer to Worker Node Configuration. |
As a manager, you want to shortlist the employees who are willing for business travel based on age and education. An input data snippet is displayed below.
We apply Categorical Naive Bayes to the input data by selecting two independent columns. The chosen values are given below.
Property | Value |
Task Name | Categorical_Naive_Bayes |
Dependent Variable | Business Travel |
Independent Variables | Age, Education |
Class Prior | None |
Alpha | 1 |
Fit Prior | True |
Minimum Categories | None |
Dimensionality Reduction | None |
Add Result as variable | None |
The result page displays the following sections.
Section 1 –
In the top right corner, the categorical variable's different options are displayed. When you select the different values, the following calculated statistical variables are displayed. The first option appears as the default selected option.
Section 2 – Confusion Matrix
Following is the confusion matrix for the specified categorical variable. It contains predicted values and actual values for the category.
Section 3 – ROC chart
The Receiver Operating Characteristic (ROC) Chart for the Business Travel is given below. ROC curve is a probability curve that helps in the measurement of the performance of a classification model at various threshold settings.
Section 4 – Lift Chart
The Lift Chart obtained for the Business Travel is given below. A lift is the measure of the effectiveness of a model. It is the ratio of the percentage gain to the percentage of random expectation at a given decile level. It is the ratio of the result obtained with a predictive model to that obtained without it.