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Is data cleaning always helpful for ML pipeline?
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Classroom Contents
Overview and Importance of Data Quality
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- 1 Overview and Importance of Data Quality for Machine Learning Tasks
- 2 Acknowledgements
- 3 Data Preparation in Machine Learning
- 4 Challenges with Data Preparation
- 5 Data Quality Analysis can help..
- 6 Different personas in enterprise setting..
- 7 To put it all together
- 8 To summarize
- 9 Data Quality Metrics
- 10 Common Data Cleaning Techniques
- 11 Is data cleaning always helpful for ML pipeline?
- 12 Insights: Impact of different cleaning techniques
- 13 In conclusion
- 14 Why it happens?
- 15 Why Imbalanced Classification is Hard?
- 16 Evaluation Metrics for Imbalanced Datasets Accuracy Paradox
- 17 Factors affecting class imbalance
- 18 Affecting Factor: Imbalance Ratio
- 19 Affecting Factor: Overlap
- 20 Affecting Factor: Smaller sub-concepts
- 21 Affecting Factor: Dataset Size
- 22 Affecting Factor: Combined Effect
- 23 Modelling Strategies: Types
- 24 Resampling Techniques
- 25 Bayes Impact index
