Machine Learning, Data Science, Deep Learning, Artificial Intelligence, Regression.
at a cubic function’s graph. x = np.arange(-5.0, 5.0, 0.1) #You can adjust the slope and intercept to verify the changes in the graph y = 1*(x**3) + 1*(x**2) + 1*x + 3 y_noise = 20 * np.random.normal(size=x.size) ydata = y + y_noise plt.plot(x, ydata, 'bo') plt.plot(x,y, 'r') plt.ylabel('Dependent