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# Create a new array with the added features: features_two
features_two = train[["Pclass","Age","Sex","Fare", "SibSp", "Parch", "Embarked"]].values

#Control overfitting by setting "max_depth" to 10 and "min_samples_split" to 5 : my_tree_two
max_depth = 10
min_samples_split = 5
my_tree_two = tree.DecisionTreeClassifier(max_depth = 10, min_samples_split = 5, random_state = 1)
my_tree_two = my_tree_two.fit(features_two, target)

#Print the score of the new decison tree
print(my_tree_two.score(features_two, target))