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Techniques to Work with Imbalanced Data for Machine Learning in Python

Techniques to Work with Imbalanced Data for Machine Learning in Python

DigitalSreeni via YouTube Direct link

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Techniques to Work with Imbalanced Data for Machine Learning in Python

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  1. 1 Intro
  2. 2 What is imbalance
  3. 3 Top 7 techniques
  4. 4 Image segmentation
  5. 5 Generate features
  6. 6 Create labels
  7. 7 Unique and counts
  8. 8 Accuracy
  9. 9 ROCAUC Score
  10. 10 Upsampling
  11. 11 moti
  12. 12 smote
  13. 13 results
  14. 14 Deep learning
  15. 15 Class weights
  16. 16 Adding weights
  17. 17 Manual class weights
  18. 18 Summary

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