Definition:
A supervised learning algorithm that classifies a data point based on the majority class of its nearest neighbors.
Key Points:
Distance-based: Uses Euclidean, Manhattan, or other distance metrics
Simple and non-parametric
Works for classification and regression
Applications:
Handwriting recognition
Image classification
Recommender systems
Advantages:
Simple to implement
No training phase required
Limitations:
Slower for large datasets
Sensitive to irrelevant or noisy features
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