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

sklearn.pipeline.make_pipeline(*steps)

Sample usage of Nearest Neighbors classification. It will plot the decision boundaries for each class.

class sklearn.linear_model.MultiTaskElasticNetCV(l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True

Linear Discriminant Analysis (

class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0

class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)

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

class sklearn.feature_extraction.text.CountVectorizer(input=u'content', encoding=u'utf-8', decode_error=u'strict',

class sklearn.covariance.ShrunkCovariance(store_precision=True, assume_centered=False, shrinkage=0.1)