Comparing randomized search and grid search for hyperparameter estimation
Comparing randomized search and grid search for hyperparameter estimation
Compare randomized search and grid search for optimizing hyperparameters of a random forest. All parameters that influence the learning are searched simultaneously (except for the number of estimators, which poses a time / quality tradeoff).
The randomized search and the grid search explore exactly the same space of parameters. The result in parameter settings is quite similar, while the run time for randomized search is drastically lower.
The performance is slightly worse for the randomized search, though this is most likely a noise effect and would not carry over to a held-out test set.
Note that in practice, one would not search over this many different parameters simultaneously using grid search, but pick only the ones deemed most important.
Python source code: randomized_search.py
print(__doc__) import numpy as np from time import time from operator import itemgetter from scipy.stats import randint as sp_randint from sklearn.grid_search import GridSearchCV, RandomizedSearchCV from sklearn.datasets import load_digits from sklearn.ensemble import RandomForestClassifier # get some data iris = load_digits() X, y = iris.data, iris.target # build a classifier clf = RandomForestClassifier(n_estimators=20) # Utility function to report best scores def report(grid_scores, n_top=3): top_scores = sorted(grid_scores, key=itemgetter(1), reverse=True)[:n_top] for i, score in enumerate(top_scores): print("Model with rank: {0}".format(i + 1)) print("Mean validation score: {0:.3f} (std: {1:.3f})".format( score.mean_validation_score, np.std(score.cv_validation_scores))) print("Parameters: {0}".format(score.parameters)) print("") # specify parameters and distributions to sample from param_dist = {"max_depth": [3, None], "max_features": sp_randint(1, 11), "min_samples_split": sp_randint(1, 11), "min_samples_leaf": sp_randint(1, 11), "bootstrap": [True, False], "criterion": ["gini", "entropy"]} # run randomized search n_iter_search = 20 random_search = RandomizedSearchCV(clf, param_distributions=param_dist, n_iter=n_iter_search) start = time() random_search.fit(X, y) print("RandomizedSearchCV took %.2f seconds for %d candidates" " parameter settings." % ((time() - start), n_iter_search)) report(random_search.grid_scores_) # use a full grid over all parameters param_grid = {"max_depth": [3, None], "max_features":