Machine Learning for Evolution Strategies by Oliver Kramer

Author:Oliver Kramer
Language: eng
Format: epub, pdf
Publisher: Springer International Publishing, Cham

(6.2)

with set containing the indices of the k-nearest neighbors of pattern in the training data set . Normalization of patterns is usually applied before the machine learning process, e.g., because different variables can come in different units.

The choice of k defines the locality of kNN. For , little neighborhoods arise in regions, where patterns from different classes are scattered. For larger neighborhood sizes, e.g. , patterns with labels in the minority are ignored. Neighborhood size k is usually chosen with the help of cross-validation. For the choice of k, grid-search or testing few typical choices like [1, 2, 5, 10, 20, 50] may be sufficient. This restriction reduces the effort for tuning the model significantly. Nearest neighbor methods are part of the scikit-learn package.

The command from sklearn import neighbors imports the scikit-learn implementation of kNN.

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