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AI & Engineering Academicsโ€บ๐ŸŒฟ AI Sproutsโ€บLessonsโ€บDecision Trees: The Algorithm You Can Draw on Paper
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AI Sprouts โ€ข Intermediateโฑ๏ธ 25 min read

Decision Trees: The Algorithm You Can Draw on Paper

Public lesson preview

Learn how decision trees work, why they're one of the most intuitive ML algorithms, and when to use them.

Programme
AI Sprouts
Level
Intermediate
Reading time
25 min read

Learning objectives

By the end of this lesson, you will understand:

  • โœ“๐ŸŽฎ The 20 Questions Analogy
  • โœ“๐ŸŒฟ Anatomy of a Tree
  • โœ“๐Ÿ“ How a Decision Tree Learns
  • โœ“โœ‚๏ธ Overfitting and Pruning
  • โœ“๐ŸŒฒ From Trees to Forests

Lesson outline

  1. ๐ŸŽฎ The 20 Questions Analogy
  2. ๐ŸŒฟ Anatomy of a Tree
  3. ๐Ÿ“ How a Decision Tree Learns
  4. Information Gain and Gini Impurity
  5. โœ‚๏ธ Overfitting and Pruning
  6. ๐ŸŒฒ From Trees to Forests
  7. โœ… Strengths and โš ๏ธ Weaknesses

Introductory preview

Most machine learning algorithms are black boxes โ€” you feed in data, something mathematical happens inside, and a prediction comes out. Decision trees are different. They are one of the few algorithms you can fully explain to a non-technical colleague, draw on a whiteboard, and still trust to make accurate predictions. ---

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