Post: Decision Trees

[taemincode]

Topic

Decision Trees

Outline

1. 📌 Introduction
   • What is a decision tree?
   • Why it’s called a "tree" 🌲
   • Common uses (classification & regression)

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2. 🧩 The Core Idea
   • Splitting data into smaller groups
   • "If…then…" style rules (like a flowchart)
   • Visual intuition: branching questions

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3. 📐 How It Works
   • Start at the root node → ask a question
   • Move down branches → refine decisions
   • End at leaf nodes → prediction 🎯

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4. 🔍 How Trees Choose Splits
   • The goal: find the best question that reduces uncertainty
   • Entropy = –Σ p log₂ p (measures "messiness")
   • Information Gain = (Entropy before split) – (Weighted entropy after split)
   • Example: splitting students by "study hours" → higher info gain = better split
   • Visual idea: show entropy dropping with a bar chart 📉

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5. 🧮 A Mini Example (Step-by-Step)
   • Dataset: 6 students → 4 pass, 2 fail an exam
   • Step 1: Calculate initial entropy
   • Step 2: Try splitting on "study hours" (high vs. low)
   • Step 3: Compute new entropies & info gain
   • Show numbers so readers see how the split is chosen

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6. 🛠️ Building a Simple Tree
   • Python + scikit-learn example
   • Dataset: Predicting if someone will play tennis 🎾
   • Visual idea: plot_tree from sklearn

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7. ⚖️ Strengths & Weaknesses
   • Strengths: Easy to understand, interpretable
   • Weaknesses: Can overfit (memorize training data)
   • Example: a too-deep tree = overly specific 🍂

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8. 🌲 Other Trees
   • 🌳 Random Forests → many trees voting together → reduces overfitting
   • 🚀 Gradient Boosted Trees (XGBoost, LightGBM, CatBoost) → build trees one after another, each fixing mistakes of the previous

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9. ✅ Summary
   • Decision trees = flowcharts for data
   • They split data into rules → predictions
   • Info Gain = the key to finding the "best questions"
   • Many variations exist to make trees stronger

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10. 🚀 What’s Next?
    • Dive deeper into Random Forests
    • Explore Gradient Boosted Trees (XGBoost, LightGBM)
    • Compare with Neural Networks for fun

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✍️ Bonus Tips
• Draw small trees by hand to practice 🌿
• Use simple datasets (like Titanic survivors 🚢)
• Show intermediate calculations for entropy & info gain 🧮
• Add comments in code for clarity ✨

Checklist

• Create branch from this issue
• Draft content
• Add diagrams/plots
• Proofread & edit
• Open PR
• Merge & publish