# Learning from Mistakes in a classification task

Let's say I have a text classifier that I have trained on some training dataset. Now when I run this trained classifier on some test dataset, I identify the cases where it went wrong (assuming I have correct labels for test dataset as well).

How can I re-train the classifier such that it learns from its mistakes? Applies to both binary and multi-class setting.

PS: Since this is a broad question, any pointers to relevant articles would suffice as well.

• Thank you so much Dougal. Your answers have been very helpful and provided the exact information that I was looking for. Just one last follow up: Why would it take me longer to adapt to concept drift? Possibly because I will be using training data that is now stale? But the incremental learning also does not really mitigates that issue. Does it? Also can you please point me to methods that are commonly used to deal with concept drift? Thank you. – The Wanderer Feb 8 '18 at 22:40