Greetings, The feature importance calculated by the RandomForest implementation is a very useful feature. I personally use it to select the best features because it is simple and fast, and then I train MLPRegressors. The limitation of this approach is that although I can control the loss function of the MLPRegressor (I have modified scikit-learn's implementation to accept an arbitrary loss function), I cannot do the same with RandomForestRegressor, and hence I have to rely on 'mse' which is not in accordance with the loss functions I use in MLPs. Today I was looking at the _criterion.pyx file:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_criterion.pyx However, the code is in Cython and I find it hard to follow. I know that for Regression the relevant class are Criterion(), RegressionCriterion(Criterion), and MSE(RegressionCriterion). My question is: is it possible to write a class that takes an arbitrary function "loss(predictions, targets)" to calculate the loss and impurity of the nodes? thanks, Thomas -- ====================================================================== 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/
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