optimal_degree

max_degree = 9
err_train = np.zeros(max_degree)    
err_cv = np.zeros(max_degree)      
x = np.linspace(0,int(X.max()),100)  
y_pred = np.zeros((100,max_degree))  #columns are lines to plot

for degree in range(max_degree):
    lmodel = lin_model(degree+1)
    lmodel.fit(X_train, y_train)
    yhat = lmodel.predict(X_train)
    err_train[degree] = lmodel.mse(y_train, yhat)
    yhat = lmodel.predict(X_cv)
    err_cv[degree] = lmodel.mse(y_cv, yhat)
    y_pred[:,degree] = lmodel.predict(x)
    
optimal_degree = np.argmin(err_cv)+1