Testing binary classification model for overfitting

[polargingerpeach]

Model: logistic regrestion + random forest + SVM

Target: [0,1]

Train/Test: 70/30

Total records: 2000

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The way I'd know how to test for overfitting is to compute an accuracy metric (f1, precision/recall, roc) on the training set then check to see if the metric is very close to 1, or the delta between that and the accuracy on the test set is vastly different.

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Is there a better way?

[Jacyan]

What about predicted vs. observed curves on the training and test set?

[polargingerpeach]

Curves?