I have a (seemingly) simple question concerning systematic studies for classification problems. Is there any literature (books, papers) describing an approach for systematic studies on classifiers, such as varying the size of the training sample, number of input variables, size of the correlation between input variables and classes on simulated data, type of classifier, configuration of parameters of the algorithm etc.?
The goal is to prove the robustness and limitations of the method before training on real data. While I have a good feeling of what can and should be done, I want to point a beginner in the right direction for a project without doing all the hard work myself.
grenouillefolle t1_j6i36hx wrote
Reply to [D] Simple Questions Thread by AutoModerator
I have a (seemingly) simple question concerning systematic studies for classification problems. Is there any literature (books, papers) describing an approach for systematic studies on classifiers, such as varying the size of the training sample, number of input variables, size of the correlation between input variables and classes on simulated data, type of classifier, configuration of parameters of the algorithm etc.?
The goal is to prove the robustness and limitations of the method before training on real data. While I have a good feeling of what can and should be done, I want to point a beginner in the right direction for a project without doing all the hard work myself.