This is great. A very useful feature would be to allow basic L_1 and L_2 regularization.
This makes it much easier to avoid problems with separable problems. It might be interesting to think for a moment how easy it would be to support generalized linear regression in this same package. Small changes to the loss function in the optimization should allow you to have not just logistic and probit regression, but also to get Poisson regression and SVM in the same framework. On Fri, Sep 7, 2012 at 3:22 AM, marios michaelidis <mimari...@hotmail.com>wrote: > I am willing to provide complete > Logistic and Probit regression algorithms, optimizable by newton Raphson > optimization maximum-likelihood method , in a very programmatically easy > way > (e.g regression(double matrix [][], double Target[], String > Constant, double precision, double tolerance) , with academic references > and > very quick (3 secs for 60k set), with getter methods for all the common > statistics such as null Deviance, Deviance, AIC, BIC, Chi-square f the > model, > betas, Wald statistics and p values, Cox_snell R square, Nagelkerke’s > R-Square, > Pseudo_r2, residuals, probabilities, classification matrix. >
