class sklearn.feature_selection.SelectPercentile(score_func=, percentile=10)

Density estimation walks the line between unsupervised learning, feature engineering, and data modeling. Some of the most popular and useful density estimation techniques

The usual covariance maximum likelihood estimate is very sensitive to the presence of outliers in the data set. In such a case, it would be better to

sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True)

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

sklearn.cluster.spectral_clustering(affinity, n_clusters=8, n_components=None, eigen_solver=None, random_state=None, n_init=10

class sklearn.ensemble.RandomForestRegressor(n_estimators=10, criterion='mse', max_depth=None, min_samples_split=2, min_samples_leaf=1

class sklearn.neighbors.KernelDensity(bandwidth=1.0, algorithm='auto', kernel='gaussian', metric='euclidean', atol=0, rtol=0, breadth_first=True

The dataset used in this example is the 20 newsgroups dataset which will be automatically downloaded and then cached and reused for

sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)