Machine Learning for Developers

Correlation fit

Now, we will define a function implementing the correlation method to find the parameters for our regression:

def correlation_fit(x, y):
    beta = correlation(x, y) * np.std(y, ddof=1) / np.std(x,ddof=1)
    alpha = np.mean(y) - beta * np.mean(x)
    return alpha, beta

Let’s then run the fitting function and print the guessed parameters:

alpha, beta = correlation_fit(X, Y)
print(alpha)
print(beta)1.08355803285
2.22994049512

Let’s now graph the regressed line with the data in order to intuitively show the appropriateness of the solution:

plt.scatter(X,Y)
xr=np.arange(0,3.5)
plt.plot(xr,(xr*beta)+alpha)

This is the final plot we will get with our recently calculated slope and intercept:

Final regressed line.