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- Fix: uses binomial (log) loss for classification - Added: use of Friedman standardisation on linear variables (Winsorised and scaled by 0.4/stdev) - Added: use of Friedman randomisation of number of terminal nodes using exponential distrbution - Fixed: use of set for rules sometimes caused wrong coeficients to be associiated with the wrong rules! Rules are now stored as a list (ie ordered) - Added ability for certain features to be constrained monotone (upcoming paper!) - Improved: sped up prediction by not evaluating rules with zero coefficients - Added: Max rules parameter like Friedman - Added: Invisible use of BoostingRegressor/Classifier (created according to constructor parameters)
Added a lot of features to make it more like the original paper, and the interface like Friedmans R implementation (http://statweb.stanford.edu/~jhf/r-rulefit/RuleFit_help.html) - Added: binomial (log) loss for classification (using glmnet_py) - Added: use of Friedman standardisation on linear variables (Winsorised and scaled by 0.4/stdev) - Added: use of Friedman randomisation of number of terminal nodes using exponential distrbution - Fixed: use of a set (i.e. unordered) for rules sometimes caused wrong coeficients to be associiated with the wrong rules! Rules are now stored as a list (ie ordered) - Improved: sped up prediction by not evaluating rules with zero coefficients - Added: Max rules parameter like Friedman - Added: Invisible use of BoostingRegressor/Classifier (created according to constructor parameters, like Friedman's R implementation)
Updated comments to describe rulefit constructor. Wherever possible it now matches Friedman's R library (http://statweb.stanford.edu/~jhf/r-rulefit/RuleFit_help.html).
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Oops, I'm new to github I just realised I didn't need to close the last pull request for it to update... |
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Great! Thanks a lot for this contribution |
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Christoph, I finally got to do all those changes. I have done everything as requested with the exception of adding the 'Cs' parameter into fit(). I put that in the constructor for RuleFit to match all the sklearn standard. Also, FYI LassoCV uses alphas and n_alphas so I had to convert Cs to alphas=1/Cs and n_alphas as needed.
Hopefully I haven't missed anything.
Cheers, Chris