# The Math Behind Decision Trees: An Exploration of Decision Trees

Math    |    Intermediate
• 18 videos | 1h 59m 58s
• Includes Assessment
• Earns a Badge
Rating 4.5 of 2 users (2)
Decision trees are an effective supervised learning technique for predicting the class or value of a target variable. Unlike other supervised learning methods, they're well-suited to classification and regression tasks. Use this course to learn how to work with decision trees and classification, distinguishing between rule-based and ML-based approaches. As you progress through the course, investigate how to work with entropy, Gini impurity, and information gain. Practice implementing both rule-based and ML-based decision trees and leveraging powerful Python visualization libraries to construct intuitive graphical representations of decision trees. Upon completion, you'll be able to create, use, and share rule-based and ML-based decision trees.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Define what's meant by classification, describing classification rules and rule-based classifier properties and limitations
Contrast rule-based and ml-based classifiers
Outline the structure of a decision tree, the process it uses to "decide," its advantages, and some core considerations when building one
Work through the creation of a decision tree and list some decision tree algorithms
Define what's meant by entropy and outline how it's used in relation to decision trees, referencing the id3 algorithm and information gain
Summarize how information gain and entropy are used in tandem
Define gini impurity and calculate it for a dataset
Split decision trees based on gini impurity
• Import modules and set up data
Decide splits for a rule-based decision tree
Define a rule-based decision tree
Illustrate the use of decision trees for continuous values
Visualize a decision tree
Create a rule-based decision tree
Train an ml-based decision tree
Use a trained ml-based decision tree to make decisions
Summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Comparing Rule-based and ML-based Models
• 4.  How Decision Trees Work
• 5.  Building a Rule-based Decision Tree
• 6.  How Entropy Works
• 7.  How Entropy and Information Gain Work Together
• 8.  How GINI Impurity Works
• 9.  Deciding Splits Based on GINI Impurity
• 10.  Setting up Datasets
• 11.  Imagine a Rule-based Decision Tree
• 12.  Creating a Basic Decision Tree
• 13.  Working with Decision Trees and Continuous Data
• 14.  Plotting a Decision Tree in a Tree Diagram
• 15.  Defining the Rules for a Rule-based Decision Tree
• 16.  Training an ML-based Decision Tree
• 17.  Testing an ML-based Decision Tree
• 18.  Course Summary

## EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

## YOU MIGHT ALSO LIKE

Rating 4.5 of 42 users (42)
Rating 4.5 of 1358 users (1358)

## PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Rating 4.0 of 4 users (4)
Rating 4.5 of 184 users (184)
Rating 4.5 of 12 users (12)