Variable Selection in Data Mining: Building a Predictive Model for Bankruptcy
We develop and illustrate a methodology for fitting models to large, complex data sets. The methodology uses standard regression techniques that make few assumptions about the structure of the data. We accomplish this with three small modifications to stepwise regression: (1) We add interactions to capture non-linearities and indicator functions to capture missing values; (2) We exploit modern decision theoretic variable selection criteria; and (3) We estimate standard error using a conservative approach that works for heteroscedastic data. Omitting any one of these modifications leads to poor performance. We illustrate our methodology by predicting the onset of personal bankruptcy among users of credit cards. This applications presents many challenges, ranging from the rare frequency of bankruptcy to the size of the available database. Only 2,244 bankruptcy events appear among some 3 million months of customer activity. To predict these, we begin with 255 features to which we add missing value indicators and pairwise interactions that expand to a set of over 67,000 potential predictors. From these, our method selects a model with 39 predictors chosen by sequentially comparing estimates of their significance to a series of thresholds. The resulting model not only avoids over-fitting the data, it also predicts well out of sample. To find half of the 1800 bankruptcies hidden in a validation sample of 2.3 million observations, one need only search the 8500 cases having the largest model predictions.
|Date of creation:||Feb 2001|
|Date of revision:|
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