Last month, DeepMind, a subsidiary of technology giant Alphabet, set Silicon Valley abuzz when it announced Gato, perhaps the most versatile artificial intelligence model in existence. Billed as a "generalist agent," Gato can perform over 600 different tasks. It can drive a robot, caption images, identify objects in pictures, and more. It is probably the most advanced AI system on the planet that isn't dedicated to a singular function. And, to some computing experts, it is evidence that the industry is on the verge of reaching a long-awaited, much-hyped milestone: Artificial General Intelligence.

This article was published as a part of the Data Science Blogathon. Random Forests are always referred to as black-box models. Let's try to crack open it and see what is inside it. Oops!!! Our plane has crashed, but fortunately, we all are safe. We are Data scientists, so we want to open the black box and see what random things have been recorded inside it. Yes, let's come to our topic.

So now that we've covered the basics of machine learning with regression models, let's move onto something a little more sophisticated: Decision Trees. What is a decision tree you ask? A decision tree is a set of questions you can ask to classify different data points. It's called a tree because it's in a tree like shape, just inverted. If you've got the weather forecast for the day, it'd be pretty easy to look at it and determine if you'd want to go play tennis that day.

In the previous article, we studied Multiple Linear Regression. One thing that I believe is that if we can correlate anything with us or our lives, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans. A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.

Gini Impurity is named after the Italian statistician Corrado Gini. Gini impurity can be understood as a criterion to minimize the probability of misclassification. To understand the definition (as shown in the figure) and exactly how we can build up a decision tree, let's get started with a very simple data-set, where depending on various weather conditions, we decide whether to play an outdoor game or not. From the definition, a data-set containing only one class will have 0 Gini Impurity. In building up the decision tree our idea is to choose the feature with least Gini Impurity as root node and so on... Let's get started with the simple data-set -- Here we see that depending on 4 features (Outlook, Temperature, Humidity, Wind), decision is made on whether to play tennis or not.