1. A method performed by one or more computers, the method comprising: receiving instructions to train a predictive model on a particular data set, wherein the particular data set includes a plurality of raw feature vectors; performing experiments to select a training strategy for use in training the predictive model on the particular data set, wherein the selected training strategy includes a binning strategy for binning the raw feature vectors before the raw feature vectors are provided to the predictive model, and wherein performing experiments comprises: selecting a plurality of training strategies, wherein each of the plurality of training strategies includes a respective binning strategy; performing a respective training experiment for each of the plurality of training strategies by training the model using the training strategy; identifying a best performing training strategy from the plurality of training strategies based on results of the respective training experiments for each of the plurality of training strategies; and generating a first new training strategy from the best performing training strategy by adjusting the binning strategy included in the best performing training strategy based on a measure of entropy of one or more features during the training of the model using the best performing training strategy, comprising, for a particular feature in the raw feature vectors, determining whether to decrease a number of bins for the particular feature based on an absolute value of a measure of entropy of the particular feature during the training of the model using the best performing training strategy; and training the predictive model on the particular data set using the selected training strategy.