plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.show()
################################################################################
### your code here! name your classifier object clf if you want the
### visualization code (prettyPicture) to show you the decision boundary
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from time import time
features_train, labels_train = RandomForestClassifier.make_classification(n_samples=10, n_features=1,
n_informative=2, n_redundant=0,
random_state=0, shuffle=False)
clf = RandomForestClassifier(max_depth=2, random_state=0)
t0 = time()
clf.fit(features_train, labels_train)
print("training time:", round(time()-t0, 3), "s")
t0 = time()
pred = clf.predict(features_test)
print("predict time:", round(time()-t0, 3), "s")
## call the method to show the updated chart.
print("Accuracy precision for Random Forest: %r" % accuracy_score(pred, labels_test))
print("10th: %r, 26th: %r, 50th: %r" % (pred[10], pred[26], pred[50]))
print("No. of predicted to be in the 'Chris'(1): %r" % sum(pred))
