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The K Nearest Neighbor Algorithm (kNN) Erik Zeitler Uppsala Database Laboratory
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2015-06-21Erik Zeitler2 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.
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2015-06-21Erik Zeitler3 Grades
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2015-06-21Erik Zeitler4 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. Different things! Solving assignments Understanding your own solution
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2015-06-21Erik Zeitler5 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
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2015-06-21Erik Zeitler6 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.
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2015-06-21Erik Zeitler7 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
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2015-06-21Erik Zeitler8 Cows and Ducks in a Plot
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2015-06-21Erik Zeitler9 Enter the Chicken
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2015-06-21Erik Zeitler10 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?
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2015-06-21Erik Zeitler11 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?
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2015-06-21Erik Zeitler12 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?
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