Machine Learning -- Multiclass Classification with Imbalanced Data-set

Classification problems having multiple classes with imbalanced dataset present a different challenge than a binary classification problem. The skewed distribution makes many conventional machine learning algorithms less effective, especially in predicting minority class examples. In order to do so, let us first understand the problem at hand and then discuss the ways to overcome those. The data set we will be using for this example is the famous "20 News groups" data set. The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups.