It is another commonly used method to handle classification problems with imbalanced data. It’s an interesting method. In simple words, this method evaluates the cost associated with misclassifying observations. Recent research has shown that cost-sensitive learning has many times outperformed sampling methods. Therefore, this method provides a likely alternative to sampling methods. Traditionally, machine learning…
This method works with the majority class. It reduces the number of observations from the majority class to make the data set balanced. When there exists a class that is in abundance, undersampling aims to reduce the size of the abundant class to balance the dataset. It uses all of the rare events but reduces…
This method works with minority classes. It replicates the observations from minority classes to balance the data. It is also known as upsampling. Oversampling can be defined as adding more copies of the minority class ) to obtain a balanced dataset. Oversampling can be a good choice when you don’t have a ton of data…
Class mismatch is also termed class/data Imbalance. Let’s say that I am offering you a free drink which I claim is both sweet and tangy but each time you take a drink, it always tastes sweet while it only tastes tangy once or twice. Given this scenario when your friend asks about the taste of…
