$k$-Nearest Neighbors is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Prediction is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.
Source of Description and Image: scikit-learn
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Clustering | 26 | 7.45% |
Retrieval | 23 | 6.59% |
General Classification | 20 | 5.73% |
Classification | 16 | 4.58% |
Dimensionality Reduction | 9 | 2.58% |
graph partitioning | 9 | 2.58% |
BIG-bench Machine Learning | 9 | 2.58% |
Image Retrieval | 8 | 2.29% |
Graph Embedding | 8 | 2.29% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |