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What is K-Nearest Neighbors?
K-Nearest Neighbors (KNN) is a simple, non-parametric machine learning algorithm used for classification and regression. It makes predictions based on the similarity between a data point and its closest neighbors in the dataset.
Definition
K-Nearest Neighbors is an algorithm that assigns a value or class to a data point by analysing the outcomes of the k most similar data points.
Key Takeaways
- KNN relies on similarity rather than a trained model.
- Works for both classification and regression problems.
- Performance depends heavily on the choice of k and distance metric.
Understanding K-Nearest Neighbors
KNN operates on the principle that similar data points tend to have similar outcomes. When a new data point is introduced, the algorithm calculates the distance between it and all existing data points, identifies the k closest ones, and uses their labels or values to make a prediction.
Because KNN does not build an explicit model, it is often referred to as a lazy learning algorithm. While simple to implement, it can become computationally expensive with large datasets.
Common distance measures include Euclidean distance, Manhattan distance, and cosine similarity, depending on the nature of the data.
Formula (If Applicable)
A common distance calculation used in KNN is Euclidean distance:
d(x, y) = √Σ(xᵢ − yᵢ)²
Where x and y are data points with multiple features.
Real-World Example
In credit scoring, KNN can classify loan applicants by comparing them with similar historical applicants and their repayment outcomes.
In e-commerce, KNN helps power recommendation systems by identifying customers with similar purchasing behaviour.
Importance in Business or Economics
KNN is useful for rapid prototyping and exploratory analysis. Businesses apply it in:
- Customer segmentation
- Fraud detection
- Recommendation engines
- Pattern recognition
Its transparency makes it easier to explain predictions compared to complex models.
Types or Variations
- Weighted KNN: Closer neighbors have more influence.
- Distance-Based KNN: Uses different distance metrics.
- Approximate KNN: Optimised for large datasets.
Related Terms
- Machine Learning
- Classification Algorithms
- Distance Metrics
- Supervised Learning
Sources and Further Reading
Quick Reference
- Core Idea: Predict based on nearest neighbors.
- Primary Parameter: Number of neighbors (k).
- Impact: Simple, intuitive predictions on small-to-medium datasets.
Frequently Asked Questions (FAQs)
How do you choose the value of k?
Through cross-validation and experimentation.
Is KNN suitable for large datasets?
Not always, it can be computationally intensive.
Does KNN require data normalization?
Yes, feature scaling improves accuracy.
