If Decision Trees Have High Variance, Why Does Bagging Actually Work?
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When I first learned about Decision Trees, everyone said the same thing: "Decision Trees have high variance." Then they immediately introduced Bagging and Random Forest. At first, I accepted it. Later, one question kept bothering me: How does training 100 Decision Trees suddenly solve the problem? After all, if one tree makes mistakes, why would building 99 more trees magically improve anything? That question completely changed how I understood ensemble learning. The Problem Isn't That Decision…
1Key Takeaways
- When I first learned about Decision Trees, everyone said the same thing: "Decision Trees have high variance." Then they immediately introduced Bagging and Random Forest.
- Later, one question kept bothering me: How does training 100 Decision Trees suddenly solve the problem?
- After all, if one tree makes mistakes, why would building 99 more trees magically improve anything?
- That question completely changed how I understood ensemble learning.
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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 when I first learned about Decision Trees, everyone said the same thing: "Decision Trees have high variance." Then they immediately introduced Bagging and Random Forest.
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