Python Training In Delhi

Decision Tree Algorithm

(Python)

Introduction to Python

Python is the fastest growing programming language preferred by most of the people. It is famous in terms of number of developers or companies using python or number of libraries that python has to offer. Python can be used in various fields such as GUI, Machine Learning, Software Development & Web Development and that is the reason python is also called ‘General Purpose Language’. There are various top most companies that are using python such as Google, Yahoo, Dropbox, YouTube or even NASA.

Why decision tree algorithm tree is used in Python?

Ø Decision tree is one of the easiest & popular algorithms of classification for better understanding and interpreting.

Ø It is used in the prediction step for predicting the responses of the given data.

Ø  Decision tree is used for both regression andclassification.

What is Classification?

Classification is basically a technique of categorizing the observations into variety of categories. So, basically in classification you take the data, analyze it and on the basis of various terms you’ll divide it into various categories.

Why we classify?

·       Classification is done for predicting the analysis for example when a person gets the mail, machine automatically predicts it as spam or a non-spam mail.

·       On the basis of the prediction it adds the irrelevant or spam mail to the respective folder.

·       Classification is used for protection purpose or to check whether the transaction is genuine or not. 

Types of Classification

There are four major types of classification,

1.    DECISION TREE

2.    RANDOM FOREST

3.    NAÏVE BAYES

4.    KNN

In this particular blog we’ll be discussing about only type of classification that is ‘DECISION TREE’.

DECISION TREE

Decision tree is basically a graphical representation of all the solutions to a decision which is based on certain conditions. Decision tree is known by this name because it starts with the root and later branches off to number of solutions.

Terminologies of decision tree

ü ROOT NODE

Root Node represents the entire population or sample which is further divided into two or more homogenous sets.

ü LEAF NODE

In leaf node, the node cannot be further divided into morenodes.

ü SPLITTING

Splitting is the third terminology of the decision tree where root node or sub node is further divided into different parts on the basis of various conditions.

ü BRANCH/SUB TREE

Branch or sub tree if formed by splitting the tree or node.

ü PRUNING

Pruning is the opposite of splitting that removes the unwanted branches from the tree.

ü PARENT/CHILD NODE

Root node is the main node and that is why it is called ‘Parent Node’ whereas all the other nodes are branched from parent node which is known as ‘Child Node’.

SkyWebcom is the foremost IT institute of training that offers the in-depth training of decision tree in Noida. Training of decision tree is offered by certified trainers that have experience of more than 32 years and training provided by them is based on live scenarios. Experts of the institute always remain updated with all the latest features brought in by the IT industry in order to offer the advanced training of decision tree algorithm to every single aspirant. Apart from training, SkyWebcom also ensures placement and has maintained the 100% record of the same from past many years. 

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