Quantitative Analysis of Machine Learning Model Performance and the need to consider explainability in it
-- Machine Learning KPIs, Cohen’s Kappa Statistic, Matthew's correlation coefficient (MCC), Confusion Matrix, Precision, Recall, G-measure, ROC Curve, Youden's J statistic, Type II Adversarial attack, R-squared, LIME, SHAP, ...--
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Synopsis:
For a long time, the AI/ML community relied on traditional evaluation metrics such as the confusion matrix, accuracy, precision, and recall for assessing the performance of machine learning models. However, the rapidly evolving field has been raising several ethical concerns, which calls for a more comprehensive evaluation scheme. In easy-to-understand language, this talk will delve into the quantitative analysis of model performance, emphasizing the critical importance of explainability. As ML models become increasingly complex and pervasive, understanding their decision-making processes is paramount. We'll explore various performance metrics, their limitations, and the growing need for transparency. Topics covered include Cohen’s Kappa Statistic, Matthew's correlation coefficient (MCC), Confusion Matrix, Precision, Recall, G-measure, ROC Curve, Youden's J statistic, Type II Adversarial attack, R-squared, LIME, SHAP, and more.
Date and Time
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- Date: 30 Dec 2024
- Time: 07:00 PM to 09:00 PM
- All times are (GMT-08:00) US/Pacific
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- Starts 24 October 2024 12:00 AM
- Ends 30 December 2024 12:00 AM
- All times are (GMT-08:00) US/Pacific
- No Admission Charge
Speakers
Dr. Vishnu S. Pendyala of San Jose State University
Quantitative Analysis of Machine Learning Model Performance and the need to consider explainability in it
Biography:
Vishnu S. Pendyala, PhD is a faculty member in Applied Data Science and an Academic Senator with San Jose State University, current chair of the IEEE Computer Society Santa Clara Valley Chapter, and IEEE Computer Society Distinguished Contributor. During his recent 3-year term as an ACM Distinguished Speaker and before that as a researcher and industry expert, he gave numerous (70+) talks in various forums such as faculty development programs, the 12th IEEE GHTC h5-index:14, h5-median:19, IEEE ANTS h5-index:15 h5-median:19, 11th and 12th IACC H5-Index: 606, H10-Index: 305, 10th ICMC H5-index: 10, h5-median: 15, IUCEE, to audiences at venues such as Stanford University, University of Bolton, Universidad de Ingeniería y Tecnología, Lima, Peru, IIIT Hyderabad, KREA, IIT Indore, IIIT Bhubaneswar. Some of these talks are available on YouTube and IEEE.tv. He is a senior member of the IEEE and has over two decades of experience in the software industry in the Silicon Valley, USA. His book, “Veracity of Big Data,” is available in several libraries, including those of MIT, Stanford, CMU, the US Congress and internationally. Two other books on machine learning and software development that he edited are also well-received and found a place in the US Library of Congress and other reputed libraries. Dr. Pendyala taught a one-week course sponsored by the Ministry of Human Resource Development (MHRD), Government of India, under the GIAN program in 2017 to Computer Science faculty from all over the country and delivered the keynote in a similar program sponsored by AICTE, Government of India in 2022. Dr. Pendyala recently served on the US government's National Science Foundation (NSF) proposal review panel. He received the Ramanujan Memorial gold medal and a shield for his college at the State Math Olympiad.
Address:One Washington Sq, San Jose State University, San Jose, United States, 95192-0250