Why Do Decision Trees Have High Variance?
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Every Machine Learning course eventually says this: "Decision Trees have high variance." When I first heard that, I accepted it and moved on. But later, I stopped and asked myself a simple question: What does that actually mean? Not the textbook definition. What is the model really doing that makes everyone call it a "high variance" algorithm? That question completely changed how I understood Decision Trees. Imagine Building Two Decision Trees Suppose you have a dataset with 10,000 customer…
1Key Takeaways
- Every Machine Learning course eventually says this: "Decision Trees have high variance." When I first heard that, I accepted it and moved on.
- But later, I stopped and asked myself a simple question: What does that actually mean?
- What is the model really doing that makes everyone call it a "high variance" algorithm?
- That question completely changed how I understood Decision Trees.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that every Machine Learning course eventually says this: "Decision Trees have high variance." When I first heard that, I accepted it and moved on.
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