Approaches to build Binary Predictive Model

Summary

A predictive model is developed and used to predict an outcome. When the dependent variable (outcome) is dichotomous or has two levels (e.g.  Credit application accepted or rejected), it is called a binary predictive model.

Binary logistic regression, Decision Tree or Neural Network Statistical Techniques can be used to build a model when target or dependent variable is binary.  Predictors can be of any type – categorical (e.g. marital status), ordinal (e.g. income level) and continuous (e.g.  Spend amount). Predictor variables are also called independent or exploratory variables.

Binary predictive model has many real life applications across industries. And binary logistic regression is one of the commonly used techniques to build the predictive model.  When binary logistic regression is used for developing a predictive model, the input predictor variables are transformed to improve the model fit. This is called variable transformation. We are limiting the discussion to predictor variable transformation.

For explaining the below approaches, a customer attrition example is considered. If a customer attrite in a period the target variable takes value as 1 otherwise 0. Customer Years with an organization is taken as a predictor variable.

Variable Transformation

    • Approach 1: Continuous Transformation
    • Approach 2: Dummy Variable Creation
    • Approach 3: WOE Variable Creation

 

 

 

 

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