If you see, you will find out that today, ensemble learnings are more popular and used by industry and rankers on Kaggle. Bootstrap aggregation, Random forest, gradient boosting, XGboost are all very important and widely used algorithms, to understand them in detail one needs to know the decision tree in depth. In this article, we are going to cover just that. Without any further due, let’s just dive right into it.

Table of Content

• Introduction to decision treeTypes of Decision TreeHow to Build a decision Tree from dataAvoid over-fitting in decision treesAdvantages and disadvantages of Decision TreeImplementing a decision tree using Python

Introduction to Decision Tree

Formally a decision tree is a graphical representation of all possible solutions to a decision. These days, tree-based algorithms are the most commonly used algorithms in case of supervised learning scenarios. They are easier to interpret and visualize with great adaptability. We can use tree-based algorithms for both regression and classification problems, However, most of the time they are used for classification problem.Let’s understand a decision tree from an example: Yesterday evening, I skipped dinner at my usual time because I was busy taking care of some stuff. Later in the night, I felt butterflies in my stomach. I thought only if_ I wasn’t hungry_, I could have gone to sleep as it is but as that was not the case, I decided to eat something. I had two options, to order something from outside or cook myself. I figured if I order, I will have to spare at least INR 250 on it. I finally decided to order it anyway as it was pretty late and I was in no mood of cooking. This complete incident can be graphically represented as shown in the following figure.

#visualization #data-science #statistics #decision-tree #machine-learning