The Math Behind Decision Trees: An Exploration of Decision Trees

Math    |    Intermediate
  • 18 videos | 1h 59m 58s
  • Includes Assessment
  • Earns a Badge
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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.


  • 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


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


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