A Decision Tree is a structure that allows us to split the dataset into branches and then make simple decisions at each level. This will allow us to arrive at the final decision by walking down the tree. Decision Trees are produced by training algorithms, which identify how we can split the data in the best possible way.

Any decision process starts at the root node at the top of the tree. Each node in the tree is basically a decision rule. Algorithms construct these rules based on the relationship between the input data and the target labels in the training data. The values in the input data are utilized to estimate the value for the output.

Now that we understand basic concept of Decision Trees, the next thing is to understand how the trees are automatically constructed. We need algorithms that can construct the optimal tree based on our data. In order to understand it, we need to understand the concept of entropy. In this context, entropy refers to information entropy...