It tells you the base rate of positive cases. The scale is log-odds and if you have any very common features then those should be included in this estimate. L_1 regularization will tend to prefer the intercept over any feature that has less than 100% prevalence. If you have features with 100% prevalence and constant weight then you might as well eliminate them anyway.
To convert a log-odds value x to a probability, use 1/(1+exp(-x)) On Wed, Dec 15, 2010 at 12:06 PM, Adrian E. Gould <[email protected]> wrote: > The intercept sometimes appears in model dissection reports. What can the > intercept's weight tell me about my model? > > >
