C5.0 algorithm

C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. Authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date".

It became quite popular after ranking #1 in the Top 10 Algorithms in Data Mining pre-eminent paper published by Springer LNCS in 2008.

C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. The training data is a set ${\displaystyle S={s_{1},s_{2},...}}$ of already classified samples. Each sample ${\displaystyle s_{i}}$ consists of a p-dimensional vector ${\displaystyle (x_{1,i},x_{2,i},...,x_{p,i})}$, where the ${\displaystyle x_{j}}$ represent attribute values or features of the sample, as well as the class in which ${\displaystyle s_{i}}$ falls.

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