CS 445 Machine Learning
Fall 2020
Week 5 Study Guide
Learning Objectives
- Define and characterize a KD Tree with respect to its purpose when incorporated into a KNN classifier, as well as its runtime bounds and performance with respect to the number of features within the KNN model
- Augment Decision Tree and KNN classifiers to include a probability with each prediction
- Compare/contrast Decision Tree and KNN classifiers. For example, the computational requirements to build the model (runtime and memory). Another would be the computational requirements (again, runtime and memory) for the model to make a prediction).
Resources
Deliverables
| Topic | Description |
| Submit PA 1 | Complete the PA and submit to Autolab and Canvas |
| Exam 1 | Complete the exam |