# Comparing AUC, logloss and accuracy scores between models

I have the following evaluation metrics on the test set, after running 6 models for a binary classification problem:

  accuracy logloss   AUC
1   19%      0.45   0.54
2   67%      0.62   0.67
3   66%      0.63   0.68
4   67%      0.62   0.66
5   63%      0.61   0.66
6   65%      0.68   0.42


I have the following questions:

• How can model 1 be the best in terms of logloss (the logloss is the closest to 0) since it performs the worst (in terms of accuracy). What does that mean ?
• How come does model 6 have lower AUC score than e.g. model 5, when model 6 has better accuracy. What does that mean ?
• Is there a way to say which of these 6 models is the best ?
• What's the ratio of the classes in your dataset? – Ben Reiniger Oct 30 at 2:40
• the ratio is around 1/5 – quant Oct 30 at 7:44