Clustering of unlabeled data can be performed with the module

4.1.1. Pipeline: chaining estimators

Linear Discriminant Analysis (

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

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]

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

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

4.8.1. Label binarization