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A Simple Guide to Decision Tree

Decision Tree is one of the most intuitive families of algorithms. It is extremely easy to understand.

As the name describes, it is a tree which helps us in making decisions. Decision trees can handle non-linearity in data. Decision trees are capable of complex methods of separation through easy computational methods.

Is the above data linearly separable?

No. This is where Decision Trees are very useful.

There are two types of Decision Trees which are based on the type of target variables (output variables):

We need to understand few basic terminologies used with Decision trees:

Decision Tree Terminology

That’s great, as we have learned all the terminology let’s dive in a little bit deeper and understand how exactly decision tree works.

Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes.

Splits are done based on

We will cover this in my future post here, I will go through the different splits in python which will give you a better understanding of how exactly the algorithm splits a node.

I hope you have got an intuition about how a Decision Tree Algorithm works.

Thanks for Reading! :) ❤

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