We present C4.5 very essential algorithms for achieving this task because of their ease and high performance. The decision tree based on entropy and information gain ( C4.5) is to build a tree by partitioning with highest information gain. For categorical variables, we partition by each level and find the best variable with highest information gain. For continuous variables, we find the best cut point to do a binary partition with highest information gain. In their work, they go to find relations between the presentation of some methods from this field and the values of some data-complexity measures, with the aim of defining the best execution method given a data set, using only the values of the measures computed on this data.