by Kumar Abhishek - Machine Learning for Imbalanced Data Reviews Summary
Ratings Breakdown
Rated 5 by 8 people
Pros from Reviews
- Sampling methods
- Cost-sensitive learning
- Threshold adjustment
- Model calibration
- Coverage of sampling techniques
- Practical approach
- Extensive coverage of evaluation metrics
- Comprehensive coverage of preprocessing techniques
Cons from Reviews
- None found
Notable Features
Tackle Imbalanced Datasets
Machine Learning Techniques
Deep Learning Techniques
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