from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

iris = load_iris()
data = iris['data']
labels = [0 if label == 0 else 1 for label in iris['target']]

training_features, testing_features, training_labels, testing_labels = train_test_split(data, labels)

model = RandomForest(n_trees=100)
model.fit(training_features, training_labels)

accuracy = sum([label == prediction for label, prediction in zip(testing_labels, model.predict(testing_features))])/len(testing_labels)

print(f'Accuracy: {accuracy}')