Exam preparation

# Start coding here... 

import numpy as np 
import matplotlib.pyplot as plt 
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score

def true_fun(X):
    return np.sin(2*np.pi*X)
np.random.seed(0)

n_samples = 30
degrees =[1,4,15]
X=np.sort(np.random.rand(n_samples))
y=true_fun(X)+np.random.randn(n_samples)*0.1