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currently only below teaser exists, which is not in any active slides.
some slides on defining a custom loss should be drawn up (with enough mathematical rigour).
Assume a use case, where the target variable can have a wide range of values across different orders of magnitude.
A possible solution would be to use a loss functions that allows for better model evaluation and comparison.
The \textbf{Mean Squared Logarithmic Absolute Error} is not strongly influenced by large values due to the logarithm.
[
\frac{1}{n} \sumin (\log(|\yi - \yih| + 1))^2
]
The text was updated successfully, but these errors were encountered:
currently only below teaser exists, which is not in any active slides.
some slides on defining a custom loss should be drawn up (with enough mathematical rigour).
Assume a use case, where the target variable can have a wide range of values across different orders of magnitude.
A possible solution would be to use a loss functions that allows for better model evaluation and comparison.
The \textbf{Mean Squared Logarithmic Absolute Error} is not strongly influenced by large values due to the logarithm.
[
\frac{1}{n} \sumin (\log(|\yi - \yih| + 1))^2
]
The text was updated successfully, but these errors were encountered: