computational time and precision trade off I know that there are a number of predictive models (generized linear ones, trees, neural network, support vector machines, knn, Naive Bayes, ...) that have been proposed to perform various analytical tasks. Now I am striving to find appropriate references about their performance when the data becames "Big". In other words, how is their performance when the data becames really big. Does the training time increase more than linear? Is there any comparative benchmark between computational time and precision when the data becames high (for the various predictive models).
A good reference can be found there http://scikit-learn.org/stable/modules/