Re: [R] goodness of prediction using a model (lm, glm, gam, brt, regression tree .... )

I think it's important to say why you're unhappy with your current measures? Are they not capturing aspects of the data you understand? I typically use several residual measures in conjunction, each has it's benefits/drawbacks. I just throw them all in a table. -- View this message in

Re: [R] goodness of prediction using a model (lm, glm, gam, brt, regression tree .... )

There are many ways to measure prediction quality, and what you choose depends on the data and your goals. A common measure for a quantitative response is mean squared error (i.e. 1/n * sum((observed - predicted)^2)) which incorporates bias and variance. Common terms for what you are looking for

[R] goodness of prediction using a model (lm, glm, gam, brt, regression tree .... )

Dear R-friends, How do you test the goodness of prediction of a model, when you predict on a set of data DIFFERENT from the training set? I explain myself: you train your model M (e.g. glm,gam,regression tree, brt) on a set of data A with a response variable Y. You then predict the value of