Erik Zeitler's kNN Examination Guide: Tips, Implementation, and Classification

1. The K Nearest Neighbor Algorithm (kNN) Erik Zeitler Uppsala Database Laboratory

2. Examination • Examination is split in two parts • Solve the assignment • Oral examination • During the oral examination • The instructor validates your program using a script • Non-working program  the examination ends immediately (“fail” grade is given)  you may re-do the examination later • The instructor will ask questions • on your implementation • on the method itself • All group members must take part in the solution. • Group members can get different grades on the same assignment. Erik Zeitler

3. Grades Erik Zeitler

4. Examination • Why do we have the oral part? Are we out to get you? • The assignments cover a good part of the course  understanding them will help you. • If you have problems solving the assignment, please ask during office hours. • The only way asking will affect your grade is that you might learn more. • Solving assignments • Understanding your own solution Different things! Erik Zeitler

5. What you need to do • Sign up for oral exam • Groups of 2 – 3 students • Forms are on my office door, P1320 • Implement a solution • Deadline: Submit by e-mail 24h before your oral exam • 1, 2: erik.zeitler@it.uu.se • 3, 4: gyozo.gidofalvi@it.uu.se • Answer the questions on the form • Bring one form per student • Prepare for oral exam: • Study the theory behind Erik Zeitler

6. K Nearest Neighbor • Basic idea: • If it walks like a duck and it quacks like a duck  Then it must be a duck • So how do we know how a duck walks and talks? • Either we ask the other ducks – or if they are unavailable – • Look at who else is walking and talking this way. Erik Zeitler

7. Duck walking and talking • Assume that a duck • has average step length 5…15 cm • quacks at a frequency 600…700 Hz • On the other hand consider a cow: • step length is 30…60 cm • a cow moos at 100…200 Hz Erik Zeitler

8. Cows and Ducks in a Plot Erik Zeitler

9. Enter the Chicken Erik Zeitler

10. Classifying you using kNN • Each of you belong to a group: • [F|STS|Int Masters|Exchange Students|Other] • Let’s classify each one using 1-NN and 3-NN • How do we select our distance measure? • How do we decide which of 1-NN and 3-NN is best? Erik Zeitler

11. Things to Consider for the Assignment • Preprocessing • What are the ranges of the different measurements? • Is one characteristic more important than another? • If so, how can we reflect this? • If not, do we need to do something else? • You can assume: no missing points, no noise • Selecting training and testing data and choosing K • Is the data sorted in any way? If so is this good or bad? • Are there different ways of subdividing the known data? • How do we know if the value of K is good or bad? Erik Zeitler

12. Things to Consider for the Assignment • Classifying unknown data • Do we need to preprocess the unknown data? • Which data set should we use to classify the unknown data? • Complexity • What is the offline part of kNN and what is the online part? • What is the complexity for the offline and online parts of kNN? Erik Zeitler