The Math Behind Decision Trees: An Exploration of Decision Trees

Math    |    Intermediate
  • 18 Videos | 2h 7m 58s
  • Includes Assessment
  • Earns a Badge
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

  • Playable
    1. 
    Course Overview
    2m 9s
    UP NEXT
  • Playable
    2. 
    How Classification Is Used
    9m 52s
  • Locked
    3. 
    Comparing Rule-based and ML-based Models
    6m 47s
  • Locked
    4. 
    How Decision Trees Work
    10m 23s
  • Locked
    5. 
    Building a Rule-based Decision Tree
    8m 42s
  • Locked
    6. 
    How Entropy Works
    7m 41s
  • Locked
    7. 
    How Entropy and Information Gain Work Together
    8m 50s
  • Locked
    8. 
    How GINI Impurity Works
    7m 19s
  • Locked
    9. 
    Deciding Splits Based on GINI Impurity
    4m 49s
  • Locked
    10. 
    Setting up Datasets
    6m 55s
  • Locked
    11. 
    Imagine a Rule-based Decision Tree
    4m 59s
  • Locked
    12. 
    Creating a Basic Decision Tree
    4m 51s
  • Locked
    13. 
    Working with Decision Trees and Continuous Data
    6m 6s
  • Locked
    14. 
    Plotting a Decision Tree in a Tree Diagram
    5m 49s
  • Locked
    15. 
    Defining the Rules for a Rule-based Decision Tree
    5m 11s
  • Locked
    16. 
    Training an ML-based Decision Tree
    8m 4s
  • Locked
    17. 
    Testing an ML-based Decision Tree
    9m 20s
  • Locked