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

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.ensemble.ExtraTreesClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1

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

Using orthogonal matching pursuit for recovering a sparse signal from a noisy measurement encoded with a dictionary print(__doc__)

class sklearn.neural_network.MLPRegressor(hidden_layer_sizes=(100, ), activation='relu', solver='adam', alpha=0.0001, batch_size='auto'

sklearn.metrics.median_absolute_error(y_true, y_pred)

class sklearn.svm.OneClassSVM(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False

This is an example of applying Non-negative Matrix Factorization and Latent Dirichlet Allocation on a

The problem solved in supervised learning