Dear all, I have a couple of short noob questions for whoever can take them. I'm from a very non-stats background so sorry for offending anybody with stupid questions ! :-)
I have been using logistic regression care of glm to analyse a binary dependent variable against a couple of independent variables. All has gone well so far. In my work I have to compare the accuracy of analysis to a C4.5 machine learning approach. With the machine learning, a straight-forward measure of the quality of the classifier is simply the percentage of correctly classified instances. I can calculate this for the resultant model by comparing predictions to original values 'manually'. My question: is this not automatically - or easily - calculated in the produced model or the summary of that model? I want to use my model in real time to produce results for new inputs. Basically this model is to be used as a classifier for a robot in real time. Can anyone suggest the best way that a produced model can be used directly in external code once the model has been developed in R? If my external code is in Java, then using jri is one option. A more efficient method would be to take the intercept and coefficients and actually code up the function in the appropriate programming language. Has anyone ever tried doing this? Apologies again for the stupid questions, but the sooner I get some of these things straight, the better. Claus ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.