Problem 4: Simulate a binary classification dataset with a single feature using a mixture of normal distributions with NumPy (Hint: Generate two data frames with the random number and a class label, and combine them together). The normal distribution parameters (np.random.normal) should be (5,2) and (-5,2) for the pair of samples. Induce a binary Decision Tree of maximum depth 2, and obtain the threshold value for the feature in the first split. How does this value compare to the empirical distribution of the feature?