RMSLE, or the Root Mean Square Logarithmic Error, is the ratio (the log) between the actual values in your data and predicted values in the model. Use RMSLE instead of RMSE if an under-prediction is worse than an over-prediction – where underestimating is more problematic than overestimating. For example, is it worse to forecast too much sales revenue or too little? Use RMSLE when your data has large numbers to predict and you don’t want to penalize large differences between the actual and predicted values (because both of the values are large numbers).
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