FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -One of the widely adopted class imbalance techniques for dealing with highly unbalanced datasets is called resampling. It consists of removing samples from the majority class (under-sampling) and/or adding more examples from the minority class (over-sampling).

Detailed explanation-2: -Imbalanced data is the number of observations is not the same for all the classes in a classification data set. If we consider a two class problem, if the data set contains 50% of one class of problem and 50% of another class of problem then it is called balanced data .

Detailed explanation-3: -Why is this a problem? Most machine learning algorithms assume data equally distributed. So when we have a class imbalance, the machine learning classifier tends to be more biased towards the majority class, causing bad classification of the minority class.