Decision trees
Decision trees are extensively-used classifiers in the ML world for their transparency on representing the rules that drive a classification/prediction. Let us ask the triple W questions to this algorithm to know more about it.
What are decision trees?
Decision trees are arranged in a hierarchical tree-like structure and are easy to explain and interpret. They are not susceptive to outliers. The process of creating a decision tree is a recursive partitioning method where it splits the training data into various groups with an objective to find homogeneous pure subgroups, that is, data with only one class.
Note
Outliers are values that lie far away from other data points and distort the data distribution.
Where are decision trees used?
Decision trees are well-suited for cases where there is a need to explain the reason for a particular decision. For example, financial institutions might need a complete description of rules that influence the credit score of a customer prior to issuing...
