Decision tree learners are powerful classifiers, which utilizes a tree structure to model the relationship among the features and the potential outcomes. The tree has a root node and decision nodes where choices are made. The choices split the data across branches that indicate the potential outcomes of a decision. The tree is terminated by leaf nodes (or terminal nodes) that denote the action to be taken as the result of the series of the decisions. After the model is created, many decision trees algorithms output the resulting structure in a human-readable format. This provides tremendous insight into how and why the model works or doesn’t work well for a particular task. This also makes decision trees particularly appropriate for applications in which the classification mechanism needs to be transparent for legal reasons, or in case the results needs to be shared with others in order to inform business practices. Decision tree models are often biased towards splits on fea