Generate Figure 4.6

Figure 4.6 In the same setting as that of figure 4.5, using one hundred models instead of five, bias, variance, and error for polynomials of order 1 to 5 . Order 1 has the smallest variance. Order 5 has the smallest bias. As the order is increased, bias decreases but variance increases. Order 3 has the minimum error. Ex[(E[rx]g(x))2x]=(E[rx])EX(g(x))2+EX[(g(x)EX[g(x)])2] Evaluate each of the three error functions with 10 equally spaced values starting from 0 and ending at 5 , i.e. np.linspace(0,5, 10) TODO: For each of the five polynomial models, print the average predictions, EX[g(x)], at np.linspace (0,5,10) lint: Average prediction at point x means computing the average value of the predictions of 100 models generated by 100 datasets. The point x should range from np.linspace (0,5,10) rODO: Generate and print a DataFrame with 5 rows, one for each order and 4 columns. The 4 columns are: - Order - Bias error - Variance error - Total error Hint: Average prediction at point x means computing the average value of the predictions of 100 models generated by 100 datasets. The point x should range from np.linspace (0,5,10) Hint: For bias error (E[rx])EX(g(x))2,E[rx]=f(x) and EX[g(x)] is the average over 100 models from the 100 datasets. Then, you can approximate bias error by average over x in np.linspace (0,5,10) of (E[rx]EX[g(x)])2. Hint: For For variance error, you need to have a nested loops (for each dataset and for x in n.linespace (0,5,10) ) to get the average variance arror. Hint: The total error is the sum of bias error and variance error