The Math Behind Decision Trees: An Exploration of Decision Trees
Math
| Intermediate
- 18 videos | 1h 59m 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
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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
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2m 9s
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9m 52s
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3. Comparing Rule-based and ML-based Models6m 47s
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4. How Decision Trees Work10m 23s
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5. Building a Rule-based Decision Tree8m 42s
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6. How Entropy Works7m 41s
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7. How Entropy and Information Gain Work Together8m 50s
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8. How GINI Impurity Works7m 19s
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9. Deciding Splits Based on GINI Impurity4m 49s
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10. Setting up Datasets6m 55s
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11. Imagine a Rule-based Decision Tree4m 59s
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12. Creating a Basic Decision Tree4m 51s
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13. Working with Decision Trees and Continuous Data6m 6s
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14. Plotting a Decision Tree in a Tree Diagram5m 49s
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15. Defining the Rules for a Rule-based Decision Tree5m 11s
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16. Training an ML-based Decision Tree8m 4s
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17. Testing an ML-based Decision Tree9m 20s
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18. Course Summary2m 14s
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