Instance-based learning

Overview

Instance-based learning (also known as memory-based or lazy learning) involves learning from training examples and generalizing to new instances based on similarity measures12.

Pros

• Flexibility: Can adapt to new data without retraining.
• Simplicity: Easy to implement and understand.

Cons

• Time Complexity: High time complexity, especially with large datasets.
• Storage Requirements: Requires storing all training data.

Examples

• K-Nearest Neighbors (KNN)
• Locally Weighted Learning (LWL)
• Case-Based Reasoning (CBR)