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Credit Scorecard Modeling With Missing Values Matlab Mathworks

Leo Migdal
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credit scorecard modeling with missing values matlab mathworks

This example shows how to handle missing values when you work with creditscorecard objects. First, the example shows how to use the creditscorecard functionality to create an explicit bin for missing data with corresponding points. Then, this example describes four different ways to "treat" the missing data to get a final credit scorecard with no explicit bins for missing values. When you create a creditscorecard object, the data can contain missing values. When using creditscorecard to create a creditscorecard object, you can set the name-value pair argument for 'BinMissingData' set to true. In this case, the missing data for numeric predictors (NaN values) and for categorical predictors (<undefined> values) is binned in a separate bin labeled <missing> that appears at the end of the bins.

Predictors with no missing values in the training data have no <missing> bin. If you do not specify the 'BinMissingData' argument or if you set 'BinMissingData' to false, the creditscorecard function discards missing observations when computing frequencies of Good and Bad, and neither the bininfo nor plotbins... The <missing> bin remains in place throughout the scorecard modeling process. The final scorecard explicitly indicates the points to be assigned to missing values for predictors that have a <missing> bin. These points are determined from the weight-of-evidence (WOE) value of the <missing> bin and the predictor's coefficient in the logistic model. For predictors without an explicit <missing> bin, you can assign points to missing values using the name-value pair argument 'Missing' in formatpoints, as described in this example, or by using one of the four...

The dataMissing table in the CreditCardData.mat file has two predictors with missing values — CustAge and ResStatus. Create a creditscorecard object using the CreditCardData.mat file to load the dataMissing table with missing values. Set the 'BinMissingData' argument to true. Apply automatic binning.

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This Example Shows How To Handle Missing Values When You

This example shows how to handle missing values when you work with creditscorecard objects. First, the example shows how to use the creditscorecard functionality to create an explicit bin for missing data with corresponding points. Then, this example describes four different ways to "treat" the missing data to get a final credit scorecard with no explicit bins for missing values. When you create a...

Predictors With No Missing Values In The Training Data Have

Predictors with no missing values in the training data have no <missing> bin. If you do not specify the 'BinMissingData' argument or if you set 'BinMissingData' to false, the creditscorecard function discards missing observations when computing frequencies of Good and Bad, and neither the bininfo nor plotbins... The <missing> bin remains in place throughout the scorecard modeling process. The fina...

The DataMissing Table In The CreditCardData.mat File Has Two Predictors

The dataMissing table in the CreditCardData.mat file has two predictors with missing values — CustAge and ResStatus. Create a creditscorecard object using the CreditCardData.mat file to load the dataMissing table with missing values. Set the 'BinMissingData' argument to true. Apply automatic binning.