Error analysis in supervised machine learning

Every supervised learning problem encounters either bias or variance error. Please refer to this page if you want to get more intuition about bias and variance error as it will help in understanding this post. Once you know where(bias or variance) your model is doing wrong, it becomes easier to get the next direction. This…

How to handle incorrectly labeled samples in the training or dev set ?

While doing error analysis, it might be revealed that your dataset has incorrectly labelled samples. These incorrectly labelled samples can be present in training set, dev set or test set. Note that dev set is also called as validation set. Incorrect labels in training set: There are two possibilities when the incorrect labels exist in…