Linear regression model

⚠️ 해당 내용은 Coursera의 Supervised Machine Learning: Regression and Classification 강의 내용을 정리한 내용입니다.

Linear regression model

• Superviesed learning - Regression model

  

Terminology

• Training set : Data used to train the model

  

  • Notation:
    • x = ‘input’ variable / feature
    • y = ‘output’ variable / ‘target’ vareable
    • m = number of training examples
    • (x, y) = singile training example
    • ($x^i, y^i)$ = (i)th training example

roadmap of machine learning

How to represent f(function)?

• $f_w,_b(x) = wx + b$ and $f(x) = wx + b$ are the same meaning.
• w, b : parameters / coefficients / weights

• Find w, b : $ŷ^i$ is close to $y^i$ for all ($x^i, y^i$)

Cost function : Squared error cost function

• Commenly used in linear regression