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Theophano Mitsa's Page
Profile Information
- Company:
- Self employed
- Seniority:
- Consultant
- Job Function:
- Predictive Modeling, Data Mining, Statistical Consulting, Medical Statistics, Artificial Intelligence
- Industry:
- Consulting
- Short Bio:
- Dr. Mitsa holds a Ph.D. degree in Electrical Engineering from the University of Rochester and is the author of 47 publications, 10 U.S. patents and the book (CRC Press, 2010) “Temporal Data Mining”. She has diverse academic and industrial experience, having served as a faculty member at the Universities of Iowa and Massachusetts and a Senior Software Engineer at GE HealthCare and Abiomed. Dr. Mitsa has received awards from the National Science Foundation, the Whitaker Foundation, HP, and the Fulbright program. Her most recent patent (8/2012), is on a decision-support system for adaptive hybrid reasoning. She is currently an Independent Data Mining/Machine Learning Consultant.
- LinkedIn Profile:
- http://theophanomitsa.com
Website for my blog and my book
Temporal Data Mining by CRC press:
http://www.crcpress.com/product/isbn/9781420089769
My blogs:
Theophano Mitsa's Blog
Cross-validation in R: a do-it-yourself and a black box approach
Posted on May 22, 2013 at 8:06am 2 Comments 0 Likes
In my previous post, we saw that R-squared can lead to a misleading interpretation of the quality of our regression fit, in terms of prediction power. One thing that R-squared offers no protection against is overfitting. On the other hand, cross validation, by allowing us to have cases in our testing set that are different from the cases in our training set, inherently offers protection against overfittting.
1.Do-it-yourself leave-one-out cross validation in R.
In this type…
ContinueUse PRESS, not R squared to judge predictive power of regression
Posted on May 12, 2013 at 9:00am 4 Comments 0 Likes
R squared, also known as coefficient of determination, is a popular measure of quality of fit in regression. However, it does not offer any significant insights into how well our regression model can predict future values. Instead, the PRESS statistic (the predicted residual sum of squares) can be used as a measure of predictive power. The PRESS statistic can be computed in the leave-one-out cross validation…
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