This document discusses the cost function known as Root Mean Squared Log Error (RMSLE). It is used in machine learning models for regression problems where both the predicted and actual values tend to be large numbers. RMSLE gives less penalty to errors when both values are large compared to the standard Root Mean Squared Error (RMSE). This is useful for problems like predicting inventory levels where missing a customer order is more important when overall volumes are small. The document provides graphical examples of how RMSLE works and why it is preferred in certain scenarios over RMSE.