def get_algorithms(algorithm):
if algorithm == util_functions.AlgorithmTypes.classification:<EOL><INDENT>clf = sklearn.ensemble.GradientBoostingClassifier(n_estimators=<NUM_LIT:100>, learn_rate=<NUM_LIT>,<EOL>max_depth=<NUM_LIT:4>, random_state=<NUM_LIT:1>,min_samples_leaf=<NUM_LIT:3>)<EOL>clf2=sklearn.ensemble.GradientBoostingClassifier(n_estimators=<NUM_LIT:100>, learn_rate=<NUM_LIT>,<EOL>max_depth=<NUM_LIT:4>, random_state=<NUM_LIT:1>,min_samples_leaf=<NUM_LIT:3>)<EOL><DEDENT>else:<EOL><INDENT>clf = sklearn.ensemble.GradientBoostingRegressor(n_estimators=<NUM_LIT:100>, learn_rate=<NUM_LIT>,<EOL>max_depth=<NUM_LIT:4>, random_state=<NUM_LIT:1>,min_samples_leaf=<NUM_LIT:3>)<EOL>clf2=sklearn.ensemble.GradientBoostingRegressor(n_estimators=<NUM_LIT:100>, learn_rate=<NUM_LIT>,<EOL>max_depth=<NUM_LIT:4>, random_state=<NUM_LIT:1>,min_samples_leaf=<NUM_LIT:3>)<EOL><DEDENT>return clf, clf2<EOL>
Gets two classifiers for each type of algorithm, and returns them.  First for predicting, second for cv error.
type - one of util_functions.AlgorithmTypes