So after our study group session, @willsa14 and I were wondering if anyone has a cheat sheet that can assist with deciding which metric should be used within a regression model or a classification model.
For Example.
Regression Matrices | Classification Matrices |
---|---|
Mean Square Error | Binary Accuracy |
Mean Absolute Error | Categorical Accuracy |
Mean Absolute Percentage Error | Sparse Categorical Accuracy |
Cosine Proximity | Top K Categorical Accuracy |
MSLE | Accuracy (common use) |
Log Cosh Error (Log Cosh) | Sparse Top K Categorical Accuracy |
Any good practices for determining when to use one over the other within a regression and classification matrice?