Database Reference
In-Depth Information
Root Mean Squared Log Error
This measurement is not as widely used as MSE and MAE, but it is used as the metric for
the Kaggle competition that uses the bike sharing dataset. It is effectively the RMSE of the
log-transformed predicted and target values. This measurement is useful when there is a
wide range in the target variable, and you do not necessarily want to penalize large errors
when the predicted and target values are themselves high. It is also effective when you care
about percentage errors rather than the absolute value of errors.
Note
The Kaggle competition evaluation page can be found at
https://www.kaggle.com/c/bike-
The function to compute RMSLE is shown here:
def squared_log_error(pred, actual):
return (np.log(pred + 1) - np.log(actual + 1))**2
