class sklearn.tree.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1

This examples shows how a classifier is optimized by cross-validation, which is done using the

sklearn.metrics.adjusted_rand_score(labels_true, labels_pred)

The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour and Virginica) with 4 attributes: sepal length, sepal width,

class sklearn.neural_network.BernoulliRBM(n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None)

A recursive feature elimination example showing the relevance of pixels in a digit classification task.

This is an example showing how scikit-learn can be used to classify documents by topics using a bag-of-words approach. This example uses

Many statistical problems require at some point the estimation of a population?s covariance matrix, which can be seen as an estimation of data set scatter plot shape.

sklearn.metrics.pairwise.paired_cosine_distances(X, Y)

class sklearn.cross_decomposition.PLSRegression(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)