Decision Tree

Offered byGreat Learning

Decision Tree
at
Great Learning
Overview

Duration	1 hour
Start from	Start Now
Total fee	Free
Mode of learning	Online
Difficulty level	Beginner
Official Website	Explore Free Course
Credential	Certificate

Decision Tree
at
Great Learning
Highlights

Earn a certificate of completion

Decision Tree
at
Great Learning
Course details

What are the course deliverables?

Entropy
Heterogeneity
Shannon's Entropy
Preventing Overfitting

More about this course

The decision tree algorithm fits in the category of supervised learning with the help of the algorithm we can solve regression and classification problems
The structure of the algorithm is of tree type in which each leaf node corresponds to a class label and the internal node of the tree represents the attributes
The discrete attributes are used in the decision tree for representing any Boolean function
The decision tree is simple to understand and interpret; it requires little data preparation but the cost of using the tree is logarithmic in the context of data points used for training the tree
It also performs well when assumptions are violated by the true model from where the data was generated

Decision Tree
at
Great Learning
Curriculum

Introduction to Decision Tree

Entropy and Heterogeneity Concept

Shannon's Entropy Decision Tree

Examples of Decision Tree

Preventing Overfitting

Decision Tree
at
Great Learning
Faculty details

Prof. Mukesh Rao

Designation : Director- Data Science Description : Prof. Mukesh Rao is an Adjunct Faculty at Great Lakes for Big Data and Machine Learning. Mukesh has over 20 years of industry experience in Market Research, Project Management, and Data Science. Mukesh has conducted over 100 corporate trainings. Data Science training covers all the stages of CRISP DM, tools and techniques used in each stage, machine learning algorithms and their application. Big Data training covers core Apache Hadoop technologies including HDFS, YARN, Map Reduce, PIG, HIVE, SQOOP, FLUME, SPARK and MongoDB.