A New Approach to Predictive Analytics Model Evaluation
Explainability: Would you accept the results of a model if there is a predictor that you find difficult to explain? Generally, if I don't understand it, I won't use it. It is imperative that there be a comfort level with how the model is arriving at results.
So, these are the rules when considering a model to be good:
- Adequate segmentation
- No choppiness
- Maximum stability
- Optimal predictability
- Multiple approaches attempted
- Optimal number of predictors
- Effortless explainability
A weight may be assigned to each of these dimensions. Coupled with the decile report, these added conditions further validate model results. Data miners produce a better product and managers design more successful programs.
Sam Koslowsky is VP of modeling solutions at Harte Hanks, a targeted marketing services company offering a wide array of integrated, multichannel and data-driven solutions. Reach him at firstname.lastname@example.org.