KRLS
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lukesonnet
R package for machine learning technique to fit flexible, interpretable functional forms for continuous and binary outcomes.
In kernels.R, if maxvector = 800, numvectorss 800 and the code would throw a bug. e.g. numbervectorss can't be greater than maxvector. A quick fix is to make the if...
The optimization for choosing lambda is working great, but with larger samples seems to spend a long time working on the 5th or 6th significant digit, so worth considering smarter...
Things that should be checked for and moved to Rcpp: - Any trace functions should be moved from `sum(diag(x))` to `trace_mat` (~5x faster) - Any multiplications of a matrix with...
All of the CPP is in one file. I'm going to split it in to different files: - KRLS functions (functions, gradients, hessians) - KRLogit functions (...) - Kernel fucntions...
Build a test suite which tests the different instances of KRLS and KRLogit. These will be fast and simple tests that just make sure that they pass, not necessarily to...
Currently we only get pwmfx for the gaussian kernel. Adding this for the linear and polynomial kernels is not difficult, it's simply a matter of taking the partial of the...
First compare golden search for KRLS with optimize. If golden search works considerably better, build it for KRlogit. If golden search is no better than or worse than KRlogit, replace...
Compare the above on MSE of predicted probabilities to latent Y* (which has noise) and NLL on predicted probabilities to observed Y. Also compare on MSE of average marginal effects....
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R package for machine learning technique to fit flexible, interpretable functional forms for continuous and binary outcomes.