As we saw in the last article introducing Decision Trees, decision trees can be used for classification or regression. But the same can be achieved with linear and logistic regression too. They are well tried and tested and are pretty useful in most situations, then why do we need decision trees? There are a few characteristics of Decision Trees that makes them stand out as useful algorithms in specific situations. 1. They are highly interpretable. If a patient falls in the l

Decision trees are an algorithm class that form the foundation for Random Forests, a class of algorithms that is extensively used in classification but can be used for regression too. This is a non-linear model, unlike linear or logistic regression models that we have seen so far. What is a Decision Tree? A decision tree algorithm is one that mimics the human decision-making process. It checks an answer to a question which can have more than one answer, branches off based on