Thanks a lot this is definitively interesting.
More advices are welcome !
On Sat, Sep 26, 2015 at 9:36 PM Sebastian Raschka
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
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 effe
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,
cla
