Platform For AI:Use the Binning component to implement the discretization of continuous features

Procedure

1. Go to the Machine Learning Designer page.

   1. Log on to the PAI console.

   2. In the left-side navigation pane, click Workspaces. On the Workspaces page, click the name of the workspace that you want to manage.

   3. In the left-side navigation pane, choose Model Training > Visualized Modeling (Designer) to go to the Machine Learning Designer page.

2. Create an empty pipeline and open the pipeline. For more information, see Prepare data.

   The following section describes the parameters:

   • Pipeline Name: Set the value to Use the Binning component to implement the discretization of continuous features.

   • Description: Enter Use the Binning component provided by PAI for the discretization of continuous features.

   • Visibility: Set the value to Visible to Me.

3. Configure the pipeline.

   1. In the component list on the left side, find and drag the Read Table component in the Data Source/Target folder to the canvas.

   2. In the component list on the left side, find and drag the Binning and Data Conversion Module components in the Financials folder to the canvas.

   3. Connect the preceding components as shown in the following figure.

4. Configure the component parameters.

   1. Click the Read Table component on the canvas. In the right-side panel, configure the parameters described in the following table.

      Tab	Parameter	Description
      Select Table	Table Name	Enter pai_online_project.iris_data.
      	Partition	The pai_online_project.iris_data table is not a partitioned table. Therefore, the Partition check box is dimmed.
      Fields Information	Source Table Columns	You do not need to manually specify this parameter. After you specify the Table Name parameter, the system synchronizes the information of columns in the table specified by the Table Name parameter to the Source Table Columns field.

   2. Click the Binning component on the canvas. In the right-side panel, configure the parameters described in the following table and use the default values for other parameters.

      Tab	Parameter	Description
      Fields Setting	Feature Columns	Select the f1, f2, f3, and f4 columns.
      Parameters Setting	Bins	Set this parameter to 10. This value indicates that continuous features are converted to 10 discrete intervals.
      	Binning Mode	Valid values: Equal Frequency, Equal Width, and Automatic Binning. If you set this parameter to Automatic Binning, you must specify the label column in binary classification scenarios. In this example, Equal Frequency is used.

   3. Click the Data Conversion Module component on the canvas. In the right-side panel, configure the parameters described in the following table and use the default values for other parameters.

      Tab	Parameter	Description
      Fields Setting	Columns without Data Conversion	Select the type column. Data in the output of this column is the same as that in the input.
      	Data Conversion Mode	Select Index.

5. Click in the upper part of the canvas to run the pipeline.

6. View the pipeline results.

   1. After you run the pipeline, right-click the Data Conversion Module component on the canvas and choose View Data > Output Port. Then, view the discretization results.

   2. Right-click the Binning component on the canvas and select Binning.

   3. Click the name of the feature that you want to view. The binning details of the feature are displayed in the following figure. The f1 feature is used in this example.

   4. Click the Charts tab to view the binning results.