The idea of a decision tree is to divide the data set into smaller data sets based on the descriptive features until you reach a small enough set that contains data points that fall under one label. Each feature of the data set becomes a root[parent] node, and the leaf[child] nodes represent the outcomes. The decision on which feature to split on is made based on resultant entropy reduction or information gain from the split. Classification problems for decision trees are often binary — True or False, Male or Female. However, decision trees can also be used to solve multi-class classification problems
Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean prediction of the individual trees.