Presenting a model is different from making a model, as the end audience is non technical and business minded. These are some thumb rules I use for making model presentation templates
1) Model Lift- How good is the model vs current effort.This is best shown by lift curves or KS statistics where you plot % Responders on X Axis and % Population on Y axis. Maximum separation between goods and bads is the KS statistic.
2) Model Robustness- What facts back up statistical validity of model output/equation ? Is there a way to test the model without executing it fully?
3) Model Assumptions- This deals with historical assumptions like which event is the model based on, data assumptions for validation and missing value treatment, capping of outliers.
The best way to convince business audiences is splitting the dataset into three random samples of 60 %,30 % and 10 % for model building, validation and testing again.
Then rerun the model equation on another random sample ,using a different seed in the RANUNI function. The KS should be similar and so should be the stats.
Ultimately models get validated or battered in the marketplace. A 1 % difference in response rates can make or lose hundreds of thousands of dollars especially in mass marketing or credit modeling. Business perspective and buy in is thus essential and so is continuous model performance feedback to avoid deterioration of model, as it will eventually deteriorate over a period of time.
DECISION STATS 