Introduction

The Imbalanced classification problem is what we face when there is a severe skew in the class distribution of our training data. Okay, the skew may not be extremely severe (it can vary), but the reason we identify imbalanced classification as a problem is because it can influence the performance on our Machine Learning algorithms.

One way the imbalance may affect our Machine Learning algorithm is when our algorithm completely ignores the minority class. The reason this is an issue is because the minority class is often the class that we are most interested in. For instance, when building a classifier to classify fraudulent and non-fraudulent transactions from various observations, the data is likely to have more non-fraudulent transactions than that of fraud — I mean think about it, it would be very worrying if we had an equal amount of fraudulent transactions as non-fraud.

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