What is an imbalanced data set? I've seen this term several times already, and have no idea what it means, can you explain what it means ?
 A: Answer: A dataset is imbalanced if the distribution of classes is not uniform. 
in other (paper reference) words: Imbalance problem occur where one of the two classes having more sample than other
classes.
Problem: The problem arising from imbalance is that accuracy can sometimes be high for a classifier although it does perform rather bad. 
Example: A simple example could be found in disease diagnostics. Assume you have a disease which only a very few people actually have. Lets assume you have a dataset of 100 people. 90 people are healthy, 10 are sick. Thus, if you construct a classifier which always predicts "healthy", you will achieve a wonderful accuracy of 90%. Yet in this specific case it is a rather bad performance because all sick people are falsely diagnosed as healthy.
What to do: Here sensitivity and specificity come in handy which are implicitly used when computing the AUC (area under the curve) for evaluation of a classifier. Wikipedia provides great further information. 
A: Imbalanced data set usually means that in a classification problem, the predicting target class distribution is skewed.
One example is fraud detection. Suppose we are building a classification algorithm on credit card transaction to tell if a transaction is fraud. In order to build such system, we need to have a training data set. BUT in the training data, the normal transactions will be dominate (say over 99% of data), and we only see very few fraud transactions. The problem with it is if we do not have certain data for certain classes, we may not model it well.
People use imbalance with different definitions, there is no specific threshold (say 80% or 99%) to say if the data is imbalanced.
