Review:
Random Forest Algorithm
overall review score: 4.5
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score is between 0 and 5
The random forest algorithm is a popular machine learning technique that operates 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 technique
- Can handle large data sets with higher dimensionality
- Reduces overfitting compared to single decision trees
Pros
- High accuracy in predicting outcomes
- Handles missing data well
- Provides feature importance for better understanding of data
Cons
- Requires more computational resources due to multiple decision trees
- May not perform well with noisy data