Reduced Error Pruning (REP) is a post-processing technique used to improve the accuracy and generalization of decision trees by eliminating nodes that do not contribute significantly to the model's performance. It is a simple and effective pruning method to avoid overfitting in decision trees.

Steps in Reduced Error Pruning:

  1. Split the Data: The dataset is split into training and validation sets. The training set is used to build the tree, while the validation set is used to evaluate the performance after pruning.

  2. Bottom-Up Pruning: Starting from the leaf nodes, each internal node is considered for pruning. A node is pruned if removing it and replacing it with its most frequent class (or a simple majority of its children) improves or does not affect the accuracy on the validation set.

  3. Error Comparison: For each node, the tree's accuracy is compared before and after pruning. If pruning the node results in a lower error rate or no change in error rate, the node is pruned.

  4. Termination: This process is repeated iteratively until no further improvement can be made by pruning.

Advantages:

  • Prevents Overfitting: By removing unnecessary nodes, reduced error pruning helps avoid overfitting, leading to better generalization.

  • Simplicity: The method is straightforward and easy to implement.

Limitations:

  • Computationally Expensive: REP requires a validation set and may be computationally expensive for large trees or datasets.

  • Over-Pruning: If not properly controlled, REP can prune too many nodes, reducing the model’s ability to capture important patterns.