CISC482 - Lecture13
Regression Practice
Dr. Jeremy Castagno
Class Business
Schedule
Today
- Review Linear Regression
- Review Logistic Regression
- Practice Problems
Review Linear Regression
Give me the function models
- Simple Linear Regression
- \(\hat{y} = \beta_0 + \beta_1 x\)
- Simple Polynomial Linear Regression
- \(\hat{y} = \beta_0 + \beta_1 x + ... + \beta_k x^k\)
- Multiple Linear Regression
- \(\hat{y} = \beta_0 + \beta_1 x_1 + ... \beta_k x_k\)
- Multiple (Variable) Polynomial Regression
- \(\hat{y} = \beta_0 + \beta_1 x_1 + \beta_2 x_1^2 + \beta_3 x_1 x_2 + \beta_4 x_2 + \beta_5 x_2^2\)
Review Logistic Regression
Logistic Regression
- We often use logistic regression for classification problems? Then why do we call it logistic regression? What is being regressed?
- What is the function for logistic regression?
- \(\hat{p}(x) = \frac{e^{\beta_0 + \beta_1 x}}{1+ e^{\beta_0 + \beta_1 x}}\)
- What do we call this non-linear function? HINT - what shape does it make?
Why not Linear Regression?
- Linear Regression is strongly affected by outliers
- Linear Regression is strongly affected by imbalanced classes
Visual
Class Activity
