Imbalanced datasets are most significant when setting a supervised Machine Learning with two or more classes.
Imbalanced data indicates that the amount of data points accessible for each class varies.
If there are two classes, balanced data means 50 percentage points for each class. Slight imbalance is not a concern for most ML approaches. As a result, if one class has 60% of the points while the other has 40%, there should be no noticeable performance reduction. Only when the imbalanced datasets of machine learning are extreme (i.e.90% for one class and 10% for the other) would typical optimization parameters or performance metrics be ineffective and require adjustment.
A common example of unbalanced data is seen in the e-mail classification issue, wherein emails are categorized as ham or spam. Typically, the quantity of spam emails is less than the number of relevant emails. As a result, using the original dispersion of two classes results in an unbalanced dataset.
It is not a good idea to use accuracy as a performance indicator for severely skewed datasets. In a binary classification issue, for example, if 90% of the points belong to the true class, a default prediction of true for all data points results in a classifier that is 90% correct, even if the classifier has not learned anything regarding the classification issue.
So how do you work with an imbalanced dataset? Before balancing the data, always divide it into training and testing sets. This ensures that the test dataset is as impartial as possible and represents a real evaluation of your model.
Balancing the data before splitting may induce bias in the test set if a few data points in the test set are generated synthetically and are well-known from the training set. The testing procedure should be as impartial as feasible.
Under-sampling strategies poses a risk of erasing essential information and altering the overall dataset dispersion typical in a certain area. As a result, under-sampling ought not to be the primary option for dealing with unbalanced datasets.
Everyone should be aware that the overall effectiveness of ML models developed on unbalanced datasets is limited by their capacity to forecast uncommon and minority points. It is essential to the accuracy and efficiency of the created models to identify and resolve the unbalance points.