Data set looks like:25000 observationsup to 15 predictors of different types: numeric, multi-class categorical, binarytarget variable is binaryWhich cross validation method is typical for this type of problems?By default I'm using K-Fold. How many folds is enough in this case? (One of the models I use is random forest, which is time consuming...)
Which cross-validation type best suits to binary classification problem
classification;cross validation
You will have best results if you care to build the folds so that each variable (and most importantly the target variable) is approximately identically distributed in each fold. This is called, when applied to the target variable, stratified k-fold. One approach is to cluster the inputs and make sure each fold contains the same number of instances from each cluster proportional to their size.