Linear Regression with Python
Introduction Our first insight into machine learning will be through the simplest model - linear regression. The goal in regression problems is to predict the value of a continuous response variable. First we'll examine linear regression, which models the relationship between a response variable and one explanatory variable. Next, we will discuss polynomial regression and regularization methods. Simple model Linear regression tries to minimize the residual sum of squares between the observed responses in the dataset, and the responses predicted by the linear approximation. Mathematically it solves a problem of the form: We'll demonstrate the process using the toy diabetes dataset, included in scikit-learn. For more details about the loading process, take a look at the previous article about loading datasets in Python . import matplotlib.pyplot as plt import numpy as np from sklearn import datasets, linear_model from sklearn.cross_validation import train_test_split