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Theophano Mitsa's Blog (2)

Cross-validation in R: a do-it-yourself and a black box approach

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…

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Added by Theophano Mitsa on May 22, 2013 at 8:06am — 2 Comments

Use PRESS, not R squared to judge predictive power of regression

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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Added by Theophano Mitsa on May 12, 2013 at 9:00am — 4 Comments

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