ullayne02/Natural-Neighbor
Knn implementation without K parameter
This tool helps data analysts and machine learning practitioners classify new data points without needing to manually define the 'K' parameter often required in nearest neighbor algorithms. You input your dataset and new data points, and it automatically determines the optimal number of neighbors to consider for accurate classification. It's designed for those who need a more adaptive and less parameter-intensive approach to classification.
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Use this if you are performing classification tasks and want to avoid the tedious process of hyperparameter tuning for the 'K' value in K-nearest neighbors.
Not ideal if you need a classification algorithm with explicit control over the number of neighbors or if you are working with extremely high-dimensional data where distance metrics become less reliable.
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Python
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Last pushed
Jun 12, 2018
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