Machine Learning Tutorial Chap 8 | Part-3 Decision Tree | Rohit Ghosh | GreyAtom
Channel: GreyAtom EduTech
Date: 12/07/2019

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 3rd in 7 videos about Decision Tree in Machine Learning. 

In this video, we will explore Spliting Criteria:Gini index & Information Gain

The Gini Index is calculated by subtracting the sum of the squared probabilities of each class from one.  It favors larger partitions.  Information Gain multiplies the probability of the class times the log (base=2) of that class probability.  Information Gain favors smaller partitions with many distinct values.  Ultimately, you have to experiment with your data and the splitting criterion. It works with categorical target variable “Success” or “Failure”. Higher the value of Gini higher the homogeneity. CART (Classification and Regression Tree) uses Gini method to create binary splits.


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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