Machine Learning: Start with linear regression
“a very simple approach for supervised learning”
“widely used statistical learning method”
Square feet | Price
\(y = Mx + b\)
Written as \(y = \beta_0 + \beta_1x\)
y is called response or target x predictor
Once we know betas we can predict value of response
Minimize the total error from each point to our line.
Bend the line to fit better
Square feet | # bedrooms | Price
cs229 Example
Simple Linear one variable
“We typically assume that the error term is independent of X.”
p71
We usually have more than one predictor.
\(Y = \beta_0 + \beta_1 X_1 + \cdots + \beta_p X_p + \epsilon\)
p72
\(\hat{y} = \hat{\beta}_0 + \beta_1 X_1 + \cdots + \beta_p X_p + \epsilon\)
RSS =
Choose \(\beta\) to \(\beta_p\) that minimizes RSS p73