Statistics for Machine Learning

Linear regression versus gradient descent

In the following code, a comparison has been made between applying linear regression in a statistical way and gradient descent in a machine learning way on the same dataset:

>>> import numpy as np 
>>> import pandas as pd 

The following code describes reading data using a pandas DataFrame:

>>> train_data = pd.read_csv("mtcars.csv")      

Converting DataFrame variables into NumPy arrays in order to process them in scikit learn packages, as scikit-learn is built on NumPy arrays itself, is shown next:

>>> X = np.array(train_data["hp"])  ; y = np.array(train_data["mpg"])  
>>> X = X.reshape(32,1); y = y.reshape(32,1) 

Importing linear regression from the scikit-learn package; this works on the least squares ...