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

The classes in the

Biclustering can be performed with the module

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

4.1.1. Pipeline: chaining estimators

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

Kernel ridge regression (KRR) [M2012]

4.8.1. Label binarization

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

There are 3 different approaches to evaluate the quality of predictions of a model: Estimator score