Hi, Thomas, in regression trees, minimizing the variance among the target values is equivalent to minimizing the MSE between targets and predicted values. This is also called variance reduction: https://en.wikipedia.org/wiki/Decision_tree_learning#Variance_reduction
Best, Sebastian > On Mar 1, 2018, at 8:27 AM, Thomas Evangelidis <teva...@gmail.com> wrote: > > > Hi again, > > I am currently revisiting this problem after familiarizing myself with Cython > and Scikit-Learn's code and I have a very important query: > > Looking at the class MSE(RegressionCriterion), the node impurity is defined > as the variance of the target values Y on that node. The predictions X are > nowhere involved in the computations. This contradicts my notion of "loss > function", which quantifies the discrepancy between predicted and target > values. Am I looking at the wrong class or what I want to do is just not > feasible with Random Forests? For example, I would like to modify the > RandomForestRegressor code to minimize the Pearson's R between predicted and > target values. > > I thank you in advance for any clarification. > Thomas > > > > > On 02/15/2018 01:28 PM, Guillaume Lemaitre wrote: >> Yes you are right pxd are the header and pyx the definition. You need to >> write a class as MSE. Criterion is an abstract class or base class (I don't >> have it under the eye) >> >> @Andy: if I recall the PR, we made the classes public to enable such custom >> criterion. However, it is not documented since we were not officially >> supporting it. So this is an hidden feature. We could always discuss to make >> this feature more visible and document it. > > > > > > -- > ====================================================================== > Dr Thomas Evangelidis > Post-doctoral Researcher > CEITEC - Central European Institute of Technology > Masaryk University > Kamenice 5/A35/2S049, > 62500 Brno, Czech Republic > > email: tev...@pharm.uoa.gr > teva...@gmail.com > > website: https://sites.google.com/site/thomasevangelidishomepage/ > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
