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
Shift bin boundaries.
Save and export a
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
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
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
|Using Binning Explorer:|
Open the Binning Explorer app.
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
|3.||Use Binning Explorer to work interactively with the binning assignments for a scorecard.|
Export the scorecard to a new
the workflow from the MATLAB command line using
|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.|
For more detailed information on this workflow, see Binning Explorer Case Study Example.