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.
|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?