Witness the power of collective intelligence. A Random Forest constructs a multitude of decision trees, each trained on random data subsets. Together, they form a robust and accurate predictor.
Each tree trains on a random sample (with replacement) of the data. This technique is called Bootstrap Aggregating or "Bagging".
To further decorrelate trees, each split only considers a random subset of features. This increases robustness against noisy or dominant features.
The final prediction combines the "votes" from all individual trees. This ensemble approach averages out errors, leading to higher accuracy.