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Overview and Importance of Data Quality for Machine Learning Tasks

Acknowledgements

Data Preparation in Machine Learning

Challenges with Data Preparation

Data Quality Analysis can help..

Different personas in enterprise setting..

To put it all together

To summarize

Data Quality Metrics

Common Data Cleaning Techniques

Is data cleaning always helpful for ML pipeline?

Insights: Impact of different cleaning techniques

In conclusion

Why it happens?

Why Imbalanced Classification is Hard?

Evaluation Metrics for Imbalanced Datasets Accuracy Paradox

Factors affecting class imbalance

Affecting Factor: Imbalance Ratio

Affecting Factor: Overlap

Affecting Factor: Smaller sub-concepts

Affecting Factor: Dataset Size

Affecting Factor: Combined Effect

Modelling Strategies: Types

Resampling Techniques

Bayes Impact index

Description:

Explore the critical role of data quality in machine learning tasks through this comprehensive conference talk from KDD 2020. Delve into data preparation challenges, quality analysis techniques, and their impact on enterprise settings. Examine common data cleaning methods and their effectiveness in ML pipelines. Investigate the complexities of imbalanced classification, including evaluation metrics, affecting factors, and modeling strategies. Gain valuable insights from IBM Research experts on navigating data quality issues to improve machine learning outcomes.

Overview and Importance of Data Quality

Association for Computing Machinery (ACM)

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#Data Science #Data Analysis #Data Cleaning #Computer Science #Machine Learning #Data Preparation

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