Performing Variable Discretization
Discretization is the process of transforming continuous variables into discrete features by creating a set of contiguous intervals, also called bins, which span the range of the variable values. Subsequently, these intervals are treated as categorical data.
Many machine learning models, such as decision trees and Naïve Bayes, work better with discrete attributes. In fact, decision tree-based models make decisions based on discrete partitions over the attributes. During induction, a decision tree evaluates all possible feature values to find the best cut-point. Therefore, the more values the feature has, the longer the induction time of the tree is. In this sense, discretization can reduce the time it takes to train the models.
Discretization has additional advantages. Data is reduced and simplified; discrete features can be easier to understand by domain experts. Discretization can change the distribution of skewed variables; when sorting...
