A decision tree is a model with a very straightforward structure that allows us to make a prediction on an output variable, based on a series of rules arranged in a tree-like structure. The output variable that we can model can be categorical, allowing us to use a decision tree to handle classification problems. Equally, we can use decision trees to predict a numerical output, and in this way we'll also be able to tackle problems where the predictive task is a regression task.
Decision trees consist of a series of split points, often referred to as nodes. In order to make a prediction using a decision tree, we start at the top of the tree at a single node known as the root node. The root node is a decision or split point, because it places a condition in terms of the value of one of the input features, and based on this decision we know whether to continue on with the left part of the tree or with the right part of the tree. We repeat this process of choosing...
