# What is Decision tree ?

## An introduction to machine learning algorithms

A decision tree is also one of the most popular models in the field of machine learning in this article I would be introducing you to decision trees and how they work

## Decision trees

A decision tree is a decision support tool that uses a tree-like 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. decision tree makes a decision based on the condition followed by the node

a tree can be made by giving branches to each of the upper nodes and follow this hierarchy decision trees can handle high-dimensional data. In general decision tree classifier has good accuracy and they are more accurate

Decision trees classify instances by sorting them down the tree from the root to some leaf nodes and further classify into them

## now let’s see a real life example

in this article, we will consider whether we can play a tennis game or not deciding upon the wheater condition and this will tell you how the model works

as you can see I have given an image below the particular image shows whether the weather can be sunny rainy or overcast

if the weather is overcast we can play the game with no worries

if the weather is sunny or rainy then we have to take some more parameters to decide whether we can play or not

now we will create more branches until we get simplified answers for eg

if the wheater is sunny we will decide on the basis of humidity

if the wheater is sunny we will decide on the basis of wind

if humidity is high then we cannot play ,if humidity is normal we can play with no worries

similarly, we will be doing some in the case of wind

if the wind is strong then unfortunately no,if the wind is weak then we can play

## conclusion

so in this article, I have introduced decision trees soon I will be bringing a very interesting hands-on project to practice

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## More from Aviral Bhardwaj

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