If Random Forest Already Reduces Variance, Why Do We Still Need Boosting?
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After learning Decision Trees, I understood why they overfit. After learning Bagging, I understood how training multiple trees makes predictions more stable. After learning Random Forest, I thought I had reached the final destination. Then I discovered another family of algorithms: Boosting. My immediate question was simple. If Random Forest already solved the problem, why did researchers invent Boosting? The answer completely changed how I think about machine learning models. The Mistake I Was…
1Key Takeaways
- After learning Decision Trees, I understood why they overfit.
- After learning Bagging, I understood how training multiple trees makes predictions more stable.
- After learning Random Forest, I thought I had reached the final destination.
- Then I discovered another family of algorithms: Boosting.
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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 after learning Decision Trees, I understood why they overfit.
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