K-Nearest Neighbors, or KNN, is a supervised learning algorithm used for classification and regression. It’s simple, intuitive, and easy to implement.
KNN predicts the label of a new data point by looking at the ‘K’ closest points in the training dataset and choosing the majority class (for classification) or averaging their values (for regression).
How KNN Works
- Choose a value for K (number of neighbors).
- Measure the distance between the new point and all training points (common metrics: Euclidean, Manhattan).
- Select the K closest neighbors.
- Vote for the most common class (classification) or average their values (regression).
- Assign this as the prediction for the new point.
It’s like asking your closest friends for advice and following the majority opinion.
Advantages of KNN
- Simple and easy to understand
- No training phase (lazy learner)
- Works well with small datasets
- Can handle multi-class problems
Disadvantages
- Computationally expensive for large datasets
- Sensitive to noisy or irrelevant features
- Choosing the right value of K is crucial
- Requires feature scaling for distance calculation
Real-World Examples
- Recommendation systems (find users with similar preferences)
- Handwriting recognition
- Credit scoring
- Medical diagnosis
- Customer segmentation
Conclusion
KNN is a simple yet effective algorithm. Its “learning by analogy” approach makes it intuitive, especially for small datasets and classification tasks.
Citations
https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/