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

After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain. The following section gives you an example

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

Linear Discriminant Analysis (

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]