Dealing with Imbalanced Datasets in ML Classification Problems | DataHour by Damini Dasgupta

An Imbalanced Classification Problem is an example of a classification problem where the classes of the response are biased or skewed. Imbalanced datasets pose a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. This results in models that have poor predictive performance. 

Reference Documents: https://drive.google.com/drive/folder...

In this DataHour, Damini will explore the following topics in detail:

1. What are highly imbalanced datasets and the problems associated around them
2. Identifying the right metrics to use in case of imbalanced classification
3. How to treat imbalanced datasets to improve your model accuracy


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