Many problems encountered when applying machine learning in practice involve predicting a "class" that takes on a continuous numeric value, yet few machine learning schemes are able to do this. This paper describes a "rational reconstruction" of M5, a method developed by Quinlan (1992) for inducing trees of regression models. In order to accommodate data typically encountered in practice it is necessary to deal effectively with enumerated attributes and with missing values, and techniques devised by Breiman et al. (1984) are adapted for this purpose. The resulting system seems to outperform M5, based on the scanty published data that is available.