sklearn.metrics.adjusted_rand_score(labels_true, labels_pred)

This example shows the use of multi-output estimator to complete images. The goal is to predict the lower half of a face given its upper half

sklearn.datasets.make_friedman1(n_samples=100, n_features=10, noise=0.0, random_state=None)

The sklearn.datasets package embeds some small toy datasets as introduced in the

Many applications require being able to decide whether a new observation belongs to the same distribution as existing observations (it is an inlier)

2.9.1. Restricted Boltzmann machines Restricted Boltzmann machines (RBM) are unsupervised nonlinear feature learners based on a probabilistic

The cross decomposition module contains two main families of algorithms: the partial least squares (PLS) and the canonical correlation analysis (CCA). These families

class sklearn.feature_selection.VarianceThreshold(threshold=0.0)

class sklearn.neighbors.KNeighborsRegressor(n_neighbors=5, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski'

class sklearn.multiclass.OneVsRestClassifier(estimator, n_jobs=1)