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Course
Machine Learning
Description
This presentation, titled "Quantitative analysis of Machine Learning model performance and the need to consider explainability," delves into various metrics used for evaluating machine learning models. It thoroughly examines fundamental classification metrics like accuracy, precision, recall, and F-score, while also discussing more advanced measures such as the Kappa Statistic and Matthews Correlation Coefficient (MCC), particularly highlighting their relevance in scenarios with imbalanced datasets. The presentation underscores the importance of model accuracy in real-world applications and briefly introduces regression metrics like R-squared and F-statistic. Additionally, it addresses challenges related to data imbalance and fairness in ML models, stressing the critical need for explainability alongside performance.
More details: https://events.vtools.ieee.org/m/442073
Video Recording: https://ieeetv.ieee.org/channels/computer-society/quantitative-analysis-of-machine-learning-model-performance-and-the-need-to-consider-explainability
Publication Date
Winter 12-30-2024
Document Type
Presentation
Keywords
Machine Learning, Model Performance, Evaluation Metrics, Explainable AI, Classification, Regression, Imbalanced Data, Kappa Statistic, Matthews Correlation Coefficient
Disciplines
Computational Engineering | Computer Engineering | Engineering
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Recommended Citation
Pendyala, Vishnu S., "Quantitative analysis of Machine Learning model performance and the need to consider explainability" (2024). Open Educational Resources. 7.
https://scholarworks.sjsu.edu/oer/7
