This submodule contains functions that approximate the feature mappings that correspond to certain kernels, as they are used for example in support vector machines (see

Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of

Clustering of unlabeled data can be performed with the module

The classes in the

Biclustering can be performed with the module

4.1.1. Pipeline: chaining estimators

4.8.1. Label binarization

Kernel ridge regression (KRR) [M2012]

Learning the parameters of a prediction function and testing it on the same data is a methodological mistake: a model that would just repeat

Linear Discriminant Analysis (