**Discretizing continuous attributes while learning Bayesian networks**

**N. Friedman** and M. Goldszmidt

Thirteenth Inter. Conf. on Machine Learning
(ICML96), 1996.

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**Abstract**We introduce a method for learning Bayesian
networks that handles the discretization of continuous variables as an
integral part of the learning process. We formally derive a criterion
based on the *Minimal Description Length* principle for choosing
the threshold values for the discretization. This new metric embodies
a tradeoff between the complexity of the learned discretization, the
complexity of the Bayesian network, and the fitness of the network as
a model of the training data. This metric has the attractive property
of *decomposition*: the discretization of each variable depends
only on the interactions between the variable and its local
neighborhood in the network. We examine other properties of this
metric that are relevant to the computation of a discretization policy
and propose an iterative algorithm
for learning a policy. Finally, we illustrate the behavior of the
discretization in applications to both supervised and unsupervised
learning.

nir@cs.huji.ac.il