Helpful examples of using k-Nearest Neighbors (KNN) machine learning algorithms in scikit-learn.
The k-Nearest Neighbors algorithm is a simple, instance-based learning method used for both classification and regression tasks.
It operates by identifying the k closest data points (neighbors) to a given query point based on a distance metric, typically Euclidean distance.
In classification, the algorithm assigns the class label that is most common among the k neighbors. In regression, it predicts the value based on the average of the k nearest neighbors’ values.
KNN is non-parametric and lazy, meaning it makes no assumptions about the data distribution and delays computation until a query is made.
This simplicity and ease of implementation make k-NN effective for small datasets, but it can be computationally intensive and less effective with large or high-dimensional data due to the cost of distance calculations and the curse of dimensionality.