Demonstrates an active learning technique to learn handwritten digits using label propagation. We start by training a label propagation model

sklearn.metrics.hamming_loss(y_true, y_pred, labels=None, sample_weight=None, classes=None)

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

sklearn.metrics.silhouette_score(X, labels, metric='euclidean', sample_size=None, random_state=None, **kwds)

class sklearn.feature_selection.SelectFdr(score_func=, alpha=0.05)

sklearn.datasets.fetch_lfw_pairs(subset='train', data_home=None, funneled=True, resize=0.5, color=False, slice_=(slice(70, 195

sklearn.preprocessing.binarize(X, threshold=0.0, copy=True)

Warning DEPRECATED class

An example of estimating sources from noisy data.

Warning DEPRECATED