  Logistic models are studied as a tool to convert output from numerical
weather forecasting systems (deterministic and ensemble) into probability
forecasts for binary events. A logistic model obtains by putting the
logarithmic odds ratio equal to a linear combination of the inputs. As any
statistical model, logistic models will suffer from over-fitting if the number
of inputs is comparable to the number of forecast instances. Computational
approaches to avoid over-fitting by regularisation are discussed, and efficient
approaches for model assessment and selection are presented. A logit version of
the so called lasso, which is originally a linear tool, is discussed. In lasso
models, less important inputs are identified and discarded, thereby providing
an efficient and automatic model reduction procedure. For this reason, lasso
models are particularly appealing for diagnostic purposes.
