Learning Objectives
In the Asynchronous Lecture
- Refresh on learning problems where the outcome is continuous.
- Discuss the most famous parametric model – the linear model – to predict a continuous outcome.
- Delve into the K Nearest Neighbors algorithm and discuss how it can offer a non-parametric approach to fitting data.
- Explore a Decision Tree model and discuss we can split data into regions to generate a non-parametric fit.
In the Synchronous Lecture
- Discuss Bagging and Random Forest models as a way of overcoming the limitations of decision trees.
- Walkthrough how to implement the models discussed this week using
caret.
If you have any questions while watching the pre-recorded material, be sure to write them down and to bring them up during the synchronous portion of the lecture.
Asynchronous Materials
The following tabs contain pre-recorded lecture materials for class this week. Please review these materials prior to the synchronous lecture.
Total time: Approx. 1 hour
The following materials were generated for students enrolled in PPOL670. Please do not distribute without permission.
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