This example studies the scalability profile of approximate 10-neighbors queries using the LSHForest with n_estimators=20 and

Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each class.

This shows an example of a neighbors-based query (in particular a kernel density estimate) on geospatial data, using a Ball Tree built upon

This example demonstrates the behaviour of the accuracy of the nearest neighbor queries of Locality Sensitive Hashing Forest as the number

This example uses the

This example shows how kernel density estimation (KDE), a powerful non-parametric density estimation technique, can be used to learn a generative model for a dataset

Sample usage of Nearest Centroid classification. It will plot the decision boundaries for each class.

Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights.