Decision trees are a simple, fast, tree-based supervised learning algorithm to solve classification problems. Though not very accurate when compared to other logistic regression methods, this algorithm comes in handy while dealing with recommender systems.

We define the decision trees with an example. Imagine a situation where you have to predict the class of flower based on its features such as petal length, petal width, sepal length, and sepal width. We will apply the decision tree methodology to solve this problem:

Now, choose a suitable question/variable to divide the data into two parts. In our case, we chose to divide the data based on petal length

*> 2.45*and*<= 2.45*. This separates flower class`setosa`

from the rest of the classes.Now, further divide the data having petal length

*>2.45*, based on the same variable with petal length*< 4.5*and*>= 4.5*, as shown in the following image.This splitting of the...