Overview of Binning Explorer

The Binning Explorer app enables you to interactively bin credit scorecard data. Use the Binning Explorer to:

Select an automatic binning algorithm with an option to bin missing data. (For more information on algorithms for automatic binning, see autobinning.)
Shift bin boundaries.
Split bins.
Merge bins.
Save and export a creditscorecard object.

Note

When using the Binning Explorer app with MATLAB Online:

The App toolbar is not available for MATLAB Online. To access Help, from the MATLAB^® command prompt, enter doc binningExplorer.
MATLAB Online does not display predictor information using three panels (Overview, Bin Information, and Predictor Information) in the Binning Explorer window. Instead, MATLAB Online displays these panels as tabs labelled Overview, Bin Information, and Predictor Information.
When performing manual binning, selected predictors are displayed in a tab in the Binning Explorer window. When you close the tab for a predictor, you do not return to the Overview panel. To return to the Overview panel, click the Overview tab.

Binning Explorer complements the overall workflow for developing a credit scorecard model. Use screenpredictors to pare down a potentially large set of predictors to a subset that is most predictive of the credit score card response variable. You can then use this subset of predictors when using Binning Explorer to create the creditscorecard object.

Using Binning Explorer:
1.	Open the Binning Explorer app. MATLAB toolstrip: On the Apps tab, under Computational Finance, click the app icon. MATLAB command prompt: Enter `binningExplorer` to open the Binning Explorer app. Enter `binningExplorer(data)` or `binningExplorer(data,Name,Value)` to open a table in the Binning Explorer app by specifying a table (`data`) as input. Enter `binningExplorer(sc)` to open a `creditscorecard` object in the Binning Explorer app by specifying a `creditscorecard` object (`sc`) as input.
2.	Import the data into the app. You can import data into Binning Explorer by either starting directly from a data set or by loading an existing `creditscorecard` object from the MATLAB workspace.
3.	Use Binning Explorer to work interactively with the binning assignments for a scorecard.
4.	Export the scorecard to a new `creditscorecard` object. Continue the workflow from the MATLAB command line using `creditscorecard` object functions from Financial Toolbox™. For more information, see `creditscorecard`.
Using `creditscorecard` Object Functions in Financial Toolbox:
5.	Fit a logistic regression model.
6.	Review and format the credit scorecard points.
7.	Score the data.
8.	Calculate the probabilities of default for the data.
9.	Validate the quality of the credit scorecard model.