subsampling

In the documentation of SciKit-Learn Random Forest classifier , it is stated that The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default). What I dont understand is that if the sample size is always the same as the input sample size than how can we talk about a random selection. There is no selection here because we use all the (and naturally the same) samples at each training. Am I missing something here? Lol4t0 I believe this part of docs answers your question In random forests (see RandomForestClassifier

I have this problem that I want to plot a data distribution where some values occur frequently while others are quite rare. The number of points in total is around 30.000. Rendering such a plot as png or (god forbid) pdf takes forever and the pdf is much too large to display. So I want to subsample the data just for the plots. What I would like to achieve is to remove a lot of points where they overlap (where the density is high), but keep the ones where the density is low with almost probability 1. Now, numpy.random.choice allows one to specify a vector of probabilities, which I've computed

I'm trying to create N balanced random subsamples of my large unbalanced dataset. Is there a way to do this simply with scikit-learn / pandas or do I have to implement it myself? Any pointers to code that does this? These subsamples should be random and can be overlapping as I feed each to separate classifier in a very large ensemble of classifiers. In Weka there is tool called spreadsubsample, is there equivalent in sklearn? http://wiki.pentaho.com/display/DATAMINING/SpreadSubsample (I know about weighting but that's not what I'm looking for.) mikkom Here is my first version that seems to be