Addressing Challenges Associated with Imbalanced Datasets in Machine Learning

A common problem that is encountered while training machine learning models is imbalanced data. An imbalanced dataset can lead to inaccurate results even when brilliant models are used to process that data. If the data is biased, the results will also be biased, which is the last thing that any of us will want from a machine learning algorithm. Let’s find out what problems an imbalanced dataset can cause and how to handle them.

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ABOUT THE AUTHOR

Anand Borad

Anand Borad works as senior marketing executive and takes care of digital and content marketing efforts for Medical devices, Connected Retail & Healthcare and New product development initiatives. He enjoys learning newer technologies and adopting it into everyday marketing practices.