03_loss_functions.py

from sklearn.linear_model import LinearRegression
import numpy as np

x = [[6], [8], [10], [14], [18]]
y = [[7], [9], [13], [17.5], [18]]
model = LinearRegression()
model.fit(x,y)

print ("Residual sum of squares = ",np.mean((model.predict(x)- y) ** 2))
print ("Variance = ",np.var([6, 8, 10, 14, 18], ddof=1))
print ("Co-variance = ",np.cov([6, 8, 10, 14, 18], [7, 9, 13, 17.5, 18])[0][1])
print ("X_Mean = ",np.mean(x))
print ("Y_Mean = ",np.mean(y))

print ("Variance = ",np.var([6, 8, 10, 14, 18], ddof=1))
print ("Co-variance = ",np.cov([6, 8, 10, 14, 18], [7, 9, 13, 17.5, 18])[0][1])

I'm new to Machine Learning, just started from your book. Wanted to understand the ddof in variance and [0], [1] in covariance