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

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

The classes in the

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

4.8.1. Label binarization

4.1.1. Pipeline: chaining estimators

Clustering of unlabeled data 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