[Verse 1] Picture a neighborhood, data points spread around Each one got coordinates, plotted on the ground When a new point arrives, asking where it belongs We check the closest neighbors, that's how we stay strong K is just a number, three or five or seven Count the nearest points, like directions to heaven Euclidean distance, measuring the space Between our query point and every other place [Chorus] K-N-N, find the closest friends Distance calculations, that's how it begins Majority vote, let the neighbors decide Classification smooth, with the algorithm's guide K-N-N, lazy learning's the way Store all the data, compute when we play [Verse 2] No training phase needed, we just memorize All the labeled samples, that's our enterprise Instance-based learning, non-parametric flow When prediction time comes, that's when we go Calculate distances to every single point Sort them ascending, keep the data joint Pick the top K values, see what class they claim Democratic process, playing the prediction game [Chorus] K-N-N, find the closest friends Distance calculations, that's how it begins Majority vote, let the neighbors decide Classification smooth, with the algorithm's guide K-N-N, lazy learning's the way Store all the data, compute when we play [Verse 3] Manhattan distance, city blocks we count Minkowski general, parameters to mount Feature scaling matters, normalize the range Or dominant dimensions will make results strange Curse of dimensionality, space gets too wide High-dimensional data makes neighbors hard to find Choose your K wisely, odd numbers prevent ties Small K means noise, large K oversimplifies [Bridge] Regression variant, average out the values Weighted by distance, inverse power rules K-D trees speed up, spatial indexing tight Ball trees for high dims, locality sensitive hashing's might Cross-validation helps us pick the perfect K Grid search parameters, find the optimal way [Chorus] K-N-N, find the closest friends Distance calculations, that's how it begins Majority vote, let the neighbors decide Classification smooth, with the algorithm's guide K-N-N, lazy learning's the way Store all the data, compute when we play [Outro] Simple but effective, intuitive and clear K-nearest neighbors, keep the concept near From recommender systems to pattern recognition This algorithm's power spans every mission
← K-means clustering | Decision tree construction (ID3, C4.5) →