Thanks a lot this is definitively interesting. More advices are welcome !
On Sat, Sep 26, 2015 at 9:36 PM Sebastian Raschka <se.rasc...@gmail.com> wrote: > Hm, > best practices for dealing with class imbalances are (still) a tricky > business I think. Typically, you see people using different sample > techniques to shift the bias towards the minority class (most often by > oversampling). I think the class weight in scikit-learn's has a (very) > similar effect here? I remember an interesting paper about a “skew > insensitive splitting criterion” using Gini and ROC but I haven’t > implemented/tried it yet: > https://www.cs.bris.ac.uk/~flach/papers/icml03-226.pdf (The Geometry of > ROC Space: > Understanding Machine Learning Metrics through ROC Isometrics). > > Best, > Sebastian > > > > On Sep 26, 2015, at 8:50 AM, Luca Puggini <lucapug...@gmail.com> wrote: > > > > Hi, > > > > I have binary output y where class 0 has much more samples than class 1. > > I am trying to understand the importance of each predictor. > > > > I do not know if the class weights should be used or not when the tree > is trained i.e. > > > > etw = ExtraTreesClassifier(n_estimators=n_estimators, max_depth = 5, > class_weight='auto') > > or > > et = ExtraTreesClassifier(n_estimators=n_estimators, max_depth = 5) > > > > Is there a preferred option or some literature about this ? > > > > Thanks, > > Luca > > -- > > Sent by mobile phone > > > > > ------------------------------------------------------------------------------ > > _______________________________________________ > > Scikit-learn-general mailing list > > Scikit-learn-general@lists.sourceforge.net > > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > -- Sent by mobile phone
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