ποΈ Week 03 - Fundamentals of Classification
Theme: Supervised Learning
Welcome to Week 3!
This week in labs, youβll dive into linear regression modeling, comparing the base R approach with the tidymodels style we will use throughout this course.
In the lecture, weβll start by considering metrics to help us assess whether a model is good. Then, in the second hour, we will cover the fundamentals of classification, another type of supervised learning. Different to regression, where we predict a continuous outcome, classification is used to predict a categorical outcome. For example, we might want to predict whether the price of a stock will go up or down (a binary outcome) or the type of animal in a picture (a multi-class outcome).
The main algorithm we will use to cover this foundational topic is logistic regression, which is a type of generalized linear model (GLM). Weβll also explore the confusion matrix and the receiver operating characteristic (ROC) curve to assess model performance.
π¨βπ« Lecture Material
π₯ Looking for lecture recordings? You can only find those on Moodle, typically a day after the lecture. If you canβt find the recordings, please contact π§ .
Material
This week, we wonβt use slides. Instead, we will use the following Quarto markdown file: