1. A system, comprising: a repository to store training data that includes a plurality of features; and one or more devices to: select a candidate condition that includes one or more of the features, estimate, after selection of the candidate condition, a weight for the candidate condition, form a new rule from the candidate condition and the weight, compare a likelihood of occurrence of the training data given a model with the new rule and a likelihood of occurrence of the training data given the model without the new rule, determine, based on the comparison, that a difference of the likelihood of occurrence of the training data in the repository given the model with the new rule and the likelihood of occurrence of the data in the repository given the model without the new rule is greater than a cost, and add the new rule to the model upon determining that the difference of the likelihood of occurrence of the training data in the repository given the new rule and the likelihood of occurrence of the training data in the repository given the model without the new rule is greater than the cost.
1. A system, comprising: a repository to store training data that includes a plurality of features; and one or more devices to: select a candidate condition that includes one or more of the features, estimate, after selection of the candidate condition, a weight for the candidate condition, form a new rule from the candidate condition and the weight, compare a likelihood of occurrence of the training data given a model with the new rule and a likelihood of occurrence of the training data given the model without the new rule, determine, based on the comparison, that a difference of the likelihood of occurrence of the training data in the repository given the model with the new rule and the likelihood of occurrence of the data in the repository given the model without the new rule is greater than a cost, and add the new rule to the model upon determining that the difference of the likelihood of occurrence of the training data in the repository given the new rule and the likelihood of occurrence of the training data in the repository given the model without the new rule is greater than the cost. 2. The system of claim 1 , where the one or more devices are further to repeat the selecting a candidate, estimating a weight, forming a new rule, comparing a likelihood of occurrence of the training data, the determining, and adding the new rule for a number of iterations.