Random Forest
Random Forest is an ensemble supervised learning algorithm composed of many individual Decision Trees that work together. It trains trees on random subsets of the data and features, averaging their predictions for output.
Frequently Asked Questions
Why does a random forest perform better than a single decision tree?▼
A single tree is highly prone to overfitting training noise. Random Forest averages predictions across hundreds of trees, canceling out individual errors.
Is Random Forest a bagging or boosting algorithm?▼
It is a Bagging (Bootstrap Aggregating) algorithm, as it trains trees in parallel on randomly sampled subsets of the dataset.
Quick Facts
- CategoryFoundational AI
- Key ApplicationFinancial risk modeling, tabular classification benchmarks, and regression analysis.
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