Machine Learning Tutorial Chap 8 | Part-2 Decision Tree | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 2019-07-12

Welcome to the #DataScienceFridays Rohit Ghosh, a deep learning scientist and an Instructor at GreyAtom will take us through Decision Tree in machine learning through this introduction series. 

A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Decision trees can be drawn by hand or created with a graphics program or specialized software. Informally, decision trees are useful for focusing discussion when a group must make a decision.    Programmatically, they can be used to assign monetary/time or other values to possible outcomes so that decisions can be automated. Decision tree software is used in data mining to simplify complex strategic challenges and evaluate the cost-effectiveness of research and business decisions. Variables in a decision tree are usually represented by circles.




This is the 2nd in 7 videos about Decision Tree in Machine Learning. 

In this video, we will explore How Decision trees work?.

Decision tree builds classification or regression models in the form of a tree structure. It breaks down a data-set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision node (e.g., Outlook) has two or more branches (e.g., Sunny, Overcast and Rainy). Leaf node (e.g., Play) represents a classification or decision. The topmost decision node in a tree which corresponds to the best predictor called root node. Decision trees can handle both categorical and numerical data.


Complete Playlist for the Course: https://bit.ly/2Q1zvK6 

After completing our 7-part Series on Decision Tree in Machine Learning, you will be able to do the following:

Understand why to use a Decision Tree
Learn how to make a Decision Tree
Learn about the challenges with Decision Tree
Learn how to deal with Over-fitting


Here’s the full syllabus of our 7-part video on Decision Tree in Machine Learning:

Decision Trees
Introduction
Representation
Intuition
Types of Decision Trees based on target variable
Terminologies Used
Splits in Decision Trees
Decision Trees - How does it work ?
Gini Index
Chi - Square
Entropy / Information Gain
Reduction in Variance                 
Shortcomings in decision Trees
Setting Constraints
Advantages of Decision Trees
Drawbacks of Decision Trees

#machinelearningtutorial #Decisiontree #DataScience101 #Greyatom

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