Review:
Random Forests
overall review score: 4.5
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score is between 0 and 5
Random forests are an ensemble learning method for classification, regression, and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Key Features
- Ensemble learning method
- Constructs multiple decision trees
- Makes predictions based on mode or mean of individual tree outputs
Pros
- Highly accurate and robust
- Handles noisy data well
- Reduces overfitting compared to single decision trees
Cons
- Can be computationally expensive for large datasets
- Black box model makes interpretation difficult