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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)