Review:
Random Forests
overall review score: 4.5
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score is between 0 and 5
Random forests is an ensemble learning method used for classification, regression and other tasks in machine learning. It operates by constructing a multitude of decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Key Features
- Ensemble learning method
- Combines multiple decision trees
- Reduces overfitting
- Can handle large datasets with high dimensionality
- Provides feature importance
Pros
- Highly accurate predictions
- Handles noisy data well
- Does not require much hyperparameter tuning
- Suitable for both classification and regression tasks
Cons
- Can be slow to train on large datasets
- Not easily interpretable compared to a single decision tree
- May not perform well if features are correlated