Working with Imbalanced Data sets
Theory Imbalanced data sets, in the context of supervised classification problems, refer to the case when the class distribution is highly skewed or disproportionate. Since general supervised learning algorithms assume them to be balanced, they perform accuracy maximization. However, this, in turn, will propagate a model bias and be addressed to some extent, when we … Read more