https://www.coursera.org/learn/competitive-data-science/lecture/SQ9Uq/regression-metrics-optimization
MAE (L1 metric/L1 loss) - some methods don't support this metric since second derivative is zero.
RMSLE
Classification metrics optimization
https://www.coursera.org/learn/competitive-data-science/lecture/hvDC5/classification-metrics-optimization-i
logloss - very popular(like MSE for regression) - all NNs by default optimize logloss for classification. RFs turn out to be very bad in terms of logloss but they can be made better.
logloss requires models to output posterior probabilities - but what does it mean? logloss is easy to implement.
accuracy -
MAE (L1 metric/L1 loss) - some methods don't support this metric since second derivative is zero.
RMSLE
Classification metrics optimization
https://www.coursera.org/learn/competitive-data-science/lecture/hvDC5/classification-metrics-optimization-i
logloss - very popular(like MSE for regression) - all NNs by default optimize logloss for classification. RFs turn out to be very bad in terms of logloss but they can be made better.
logloss requires models to output posterior probabilities - but what does it mean? logloss is easy to implement.
accuracy -
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