In a previous couple of articles, we explored some basic machine learning algorithms. Thus far we covered some simple regression algorithms, classification algorithms and we started with algorithms that can be used for both types of problems. We used technologies like TensorFlow, Pytorch and SciKit Learn for the implementation and application of these algorithms. Apart from that we used optimization techniques such as Gradient Descent. So far we have covered:

• Linear Regression
• Simple Classification Algorithms
• Support Vector Machine

In this article, we explore Decision Trees. Just like SVM, Decision Tree is capable of performing both classification and regression tasks. They are one of the most popular machine learning algorithms. The secret of its popularity lies within its simplicity. Also, they are able to produce results with a small amount of data and solutions it provides are easily explainable. This algorithm is exceptionally useful when it is used in ensemble learning, ie. they are an integral part of Random Forest, one of the most powerful machine learning algorithms. Random Forest is just a set of multiple Decision Trees, but more on that later.

The code  that accompanies this article can be found here.

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