Hyper-parameters are parameters that are not directly learnt within estimators. In scikit-learn they are passed as arguments to the constructor of

sklearn.feature_extraction.image.extract_patches_2d(image, patch_size, max_patches=None, random_state=None)

sklearn.metrics.silhouette_score(X, labels, metric='euclidean', sample_size=None, random_state=None, **kwds)

Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets

In this example we see how to robustly fit a linear model to faulty data using the RANSAC algorithm.

class sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(priors=None, reg_param=0.0, store_covariances=False

class sklearn.decomposition.PCA(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None)

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

sklearn.ensemble.partial_dependence.plot_partial_dependence(gbrt, X, features, feature_names=None, label=None

class sklearn.neighbors.BallTree BallTree for fast generalized N-point problems BallTree(X, leaf_size=40, metric=