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5 Techniques to work with Imbalanced Data in Machine Learning
Essential guide to handle an imbalanced dataset
For classification tasks, one may encounter situations where the target class label is un-equally distributed across various classes. Such conditions are termed as an Imbalanced target class. Modeling an imbalanced dataset is a major challenge faced by data scientists, as due to the presence of an imbalance in the data the model becomes biased towards the majority class prediction.
Hence, handling the imbalance in the dataset is essential prior to model training. There are various things to keep in mind while working with imbalanced data. In this article, we will discuss various techniques to handle class imbalance to train a robust and well-fit machine learning model.
Checklist:
1) Upsampling Minority Class
2) Downsampling Majority Class
3) Generate Synthetic Data
4) Combine Upsampling & Downsampling Techniques
5) Balanced Class Weight
Before processing to discuss the 5 above-mentioned techniques, let’s focus on choosing the right metric for an Imbalanced dataset task. Choosing an incorrect metric such as Accuracy seems to perform well but actually is biased towards the majority class label. The alternate choice of performance metrics can be: