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Martin Thoma
Martin Thoma
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In regression problems, you can use various different metrics to check how well your model is doing:

  • Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better
  • Root Mean Squared Error (RMSE): In $[0, \infty)$, the smaller the better
  • Median Absolute Error (MAE): In $[0, \infty)$, the smaller the better
  • Mean Squared Log Error (MSLE): In $[0, \infty)$, the smaller the better
  • R², coefficient of determination: In $(-\infty, 1]$, the bigger the better not necessarily the bigger the better

Are there any strong reasons not to use one or the other?

In regression problems, you can use various different metrics to check how well your model is doing:

  • Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better
  • Root Mean Squared Error (RMSE): In $[0, \infty)$, the smaller the better
  • Median Absolute Error (MAE): In $[0, \infty)$, the smaller the better
  • Mean Squared Log Error (MSLE): In $[0, \infty)$, the smaller the better
  • R², coefficient of determination: In $(-\infty, 1]$, the bigger the better

Are there any strong reasons not to use one or the other?

In regression problems, you can use various different metrics to check how well your model is doing:

  • Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better
  • Root Mean Squared Error (RMSE): In $[0, \infty)$, the smaller the better
  • Median Absolute Error (MAE): In $[0, \infty)$, the smaller the better
  • Mean Squared Log Error (MSLE): In $[0, \infty)$, the smaller the better
  • R², coefficient of determination: In $(-\infty, 1]$ not necessarily the bigger the better

Are there any strong reasons not to use one or the other?

Source Link
Martin Thoma
Martin Thoma
  • 19.2k
  • 36
  • 95
  • 170

MAD vs RMSE vs MAE vs MSLE vs R²: When to use which?

In regression problems, you can use various different metrics to check how well your model is doing:

  • Mean Absolute Deviation (MAD): In $[0, \infty)$, the smaller the better
  • Root Mean Squared Error (RMSE): In $[0, \infty)$, the smaller the better
  • Median Absolute Error (MAE): In $[0, \infty)$, the smaller the better
  • Mean Squared Log Error (MSLE): In $[0, \infty)$, the smaller the better
  • R², coefficient of determination: In $(-\infty, 1]$, the bigger the better

Are there any strong reasons not to use one or the other?