Data Science – How To Handle Data Imbalance ?


Data Science – How To Handle Data Imbalance?

Table Of Contents:

  1. Resampling Techniques.
  2. Class Weighting.
  3. Algorithmic Approach.
  4. Evaluation Metric Adjustments.
  5. Data Augmentation.
  6. Anomaly Detection Approach. 

(1) Resampling Techniques

Types Of Resampling Techniques:
When To Use Which Sampling Techniques ?

(2) Class Weight Balance Technique

Python Implementation
When To Use Class Weight Technique:

(3) Algorithmic Approach Of Handling Class Imbalance.

(4) Evaluation Metric Adjustments.

Can We Use ROC – AUC for Imbalanced Class ?

(5) Data Augmentation Techniques to Handle Class Imbalance

Common Data Augmentation Techniques for Imbalanced Data

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