Fighting Churn with Data

9 Forecast accuracy and machine learning

This chapter covers

• Calculating measurements of forecasting accuracy for churn
• Backtesting a model in a historical simulation
• Setting the regression parameter for the minimum metric contribution
• Picking the best value of the regression parameter by testing (cross-validation)
• Forecasting churn risk with the XGBoost machine learning model
• Setting the parameters of the XGBoost model with cross-validation

You know how to forecast the probability of customer churn, and you also know how to check the calibration of your forecasts. Another important measurement of a forecasting model is whether the customers predicted to be highly at risk are really more at risk than those predicted to be safe. This type ...