sklearn.svm.libsvm.predict() Predict target values of X given a model (low-level method)

sklearn.preprocessing.normalize(X, norm='l2', axis=1, copy=True, return_norm=False)

sklearn.metrics.make_scorer(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs)

sklearn.metrics.pairwise.kernel_metrics()

sklearn.datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2, n_repeated=0, n_classes=2

Statistical learning

class sklearn.neighbors.DistanceMetric DistanceMetric class This class provides a uniform interface to

This example shows how to perform univariate feature selection before running a SVC (support vector classifier) to improve the classification

class sklearn.preprocessing.LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality using cross-validation. ROC