Imbalanced Dataset in Machine Learning

In Machine Learning, an imbalanced dataset is primarily concerned in the context of supervised machine learning where we are dealing with two or more classes. In this article, I’ll walk you through what an imbalanced dataset is in Machine Learning and how to deal with it.

What is Imbalanced Dataset?

imbalanced dataset

An imbalance dataset means that the number of data points available for different classes is different. If there are two classes, balanced data would mean 50% points for each of the classes. For most machine learning algorithms, a slightly unbalanced dataset is not a problem.

So, if there are 60% points for one class and 40% for the other class, it should not lead to significant performance degradation. But when the class imbalance is too high, up to 90% for one class and 10% for the other, standard optimization criteria or performance measures may not be as effective, so this type of data will require modification.

A real-time example of unbalanced data is seen in the email classification problem, where emails are classified as spam or not spam. The number of spam emails is usually less than the number of relevant emails. Thus, using the original two-class distribution leads to an unbalanced dataset.

Using precision as a measure of performance for very unbalanced data sets is not a good idea. For example, in the binary classification problem, if 90% of the points belong to the true class, a default prediction of true for all data points will result in a 90% accurate classifier, even though the classifier has learned nothing about the classification problem.

How to Deal with Imbalanced Datasets

Now, there are different approaches to deal with the problem of imbalanced datasets, some popular approaches are:

  1. Undersampling methods
  2. Oversampling methods
  3. Synthetic data generation
  4. Cost-sensitive learning
  5. Ensemble methods

Among all these approaches, the best is the generation of synthetic data which is done using the SMOTE method in machine learning. In the synthetic data generation technique, we overcome data imbalances by generating artificial data.

You can learn more about SMOTE for dealing with class imbalances from below:

I hope you liked this article on what are imbalanced datasets in Machine Learning and how to deal with them. Feel free to ask your valuable questions in the comments section below.

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Aman Kharwal

I am a programmer from India, and I am here to guide you with Data Science, Machine Learning, Python, and C++ for free. I hope you will learn a lot in your journey towards Coding, Machine Learning and Artificial Intelligence with me.

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