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Rename K-nearest neighbor (KNN).md to knn.md

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Ashita Prasad 2024-06-08 10:20:05 +05:30 zatwierdzone przez GitHub
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contrib/machine-learning/K-nearest neighbor (KNN).md → contrib/machine-learning/knn.md
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@ -119,4 +119,4 @@ plt.show()
- **Feature Scaling:** Since KNN relies on distance calculations, features should be scaled (standardized or normalized) to ensure that all features contribute equally to the distance computation.
- **Distance Metrics:** The choice of distance metric (Euclidean, Manhattan, etc.) can affect the performance of the algorithm.
In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance.
In conclusion, KNN is a versatile and easy-to-implement algorithm suitable for various classification and regression tasks, particularly when working with small datasets and well-defined features. However, careful consideration should be given to the choice of K, feature scaling, and distance metrics to optimize its performance.