Advanced Machine Learning with R

Class imbalance problem

Let's assume that one needs to build a classifier that identifies cat and dog images. The problem has two classes namely cat and dog. If one were to train a classification model, training data is required. The training data in this case is based on images of dogs and cats given as input so a supervised learning model can learn the features of dogs versus cats.

It may so happen that if there are 100 images available for training in the dataset and 95 of them are dog pictures, five of them are cat pictures. This kind of unequal representation of different classes in a training dataset is termed as a class imbalance problem.

Most ML techniques work best when the number of examples in each class are roughly equal. One ...