sklearn.neighbors.kneighbors_graph(X, n_neighbors, mode='connectivity', metric='minkowski', p=2, metric_params=None, include_self=False

An example showing how the scikit-learn can be used to recognize images of hand-written digits. This example is commented in the

class sklearn.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)

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

class sklearn.model_selection.GroupShuffleSplit(n_splits=5, test_size=0.2, train_size=None, random_state=None)

Incremental principal component analysis (IPCA) is typically used as a replacement for principal component analysis (PCA) when the dataset to be decomposed is too large to fit

Computes Lasso Path along the regularization parameter using the LARS algorithm on the diabetes dataset. Each color represents a different feature of the coefficient vector

4.1.1. Pipeline: chaining estimators

class sklearn.feature_selection.SelectKBest(score_func=, k=10)

sklearn.pipeline.make_pipeline(*steps)