Hi, the only current options for deciding on feature splits in trees / forests are 'entropy' and 'gini', two questions on this:
- is anyone planning on implementing others? - how feasible would it be to have the option of passing custom function to the tree or forest to use in splitting? We are looking into using random forest for gravitational wave candidate event classification and potentially useful behaviour modification would be to introduce asymmetric splitting functions. I see that sample weighting has recently been implemented for trees: I would like to know how far the behaviour of the splitting can be influenced by assigning the training data different weights for different classes (which might effectively lead to an asymmetric function). Thanks, Thomas -- ----------------------------------------- Institute for Gravitational Physics (Albert Einstein Institute) Callinstr. 38 D-30167 Hannover, Germany ------------------------------------------------------------------------------ November Webinars for C, C++, Fortran Developers Accelerate application performance with scalable programming models. Explore techniques for threading, error checking, porting, and tuning. Get the most from the latest Intel processors and coprocessors. See abstracts and register http://pubads.g.doubleclick.net/gampad/clk?id=60136231&iu=/4140/ostg.clktrk _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
