It's easy to understand what bias and variance mean in general in machine learning. The link below makes this very clear. But, its always hard to figure out which classifiers are of high/low bias and variance. Each classifier would have its own set of tuning parameters to alter this characteristic.
So, how will one determine whether a given classifier is of high bias or high variance?
http://scott.fortmann-roe.com/docs/BiasVariance.html
The following article discusses a chance-corrected and maximum-corrected index of predictive accuracy, that is based on the sensitivity of the model in accurately classifying observations in the various classes that are being discriminated. This index can be applied to any classification methodology, and can be used to directly compare model performance.
https://www.researchgate.net/publication/291947229_Using_data_mining_techniques_to_characterize_participation_in_observational_studies
Article Using data mining techniques to characterize participation i...
The following article discusses a chance-corrected and maximum-corrected index of predictive accuracy, that is based on the sensitivity of the model in accurately classifying observations in the various classes that are being discriminated. This index can be applied to any classification methodology, and can be used to directly compare model performance.
https://www.researchgate.net/publication/291947229_Using_data_mining_techniques_to_characterize_participation_in_observational_studies
Article Using data mining techniques to characterize participation i...
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excellent question ; the methods i know are mainly of the resampling kind
a good starting point :
http://www.csse.monash.edu.au/~webb/Files/WebbConilione06.pdf
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