Python Data Analysis Cookbook

Scikit-learn

The following argument turns seed into a numpy.random.RandomState instance:

sklearn.utils.check_random_state(seed)

The following performs a grid search over given hyperparameter values for an estimator:

sklearn.grid_search.GridSearchCV estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise')

The following argument splits arrays into random train and test sets:

sklearn.cross_validation.train_test_split(*arrays, **options)

The following returns the accuracy classification score:

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)