Clustering of unlabeled data can be performed with the module

4.8.1. Label binarization

Linear Discriminant Analysis (

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

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

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

Biclustering can be performed with the module

The classes in the

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