Prediction and Understanding

Leo Breiman (UC Berkeley) (I)

A brief overview of recent algorithmic developments inMachine Learning is given.
We note that in scientific projects, often prediction is not enough. Prediction needs to be combined with understanding of the data. Random forests, a recent development, is described and appliations given to chemistry, genetics and astronomy which emphasize the understanding of the data that can be given as by=products of the prediction process.

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