IITM MLT Week 7 Questions
K-nearest Neighbors and Decision Trees
Week 7 question PDF
Practice classification using nearby examples and feature-based splits. Work through K-nearest neighbors, decision boundaries, entropy, information gain and tree depth using the datasets in the PDF.
The complete question statements, options, numerical data and any supporting graphs or tables are in the PDF. Numbered comprehension questions can include several subquestions. Answer keys are included for checking your attempt; these are not full worked solutions.
Topics covered in this collection
- K-nearest neighbors (K-NN)
- Decision boundaries and the choice of k
- Entropy and information gain
- Decision tree splits and depth
How to approach these questions
- Read whether a training point counts as its own neighbor before calculating training errors.
- Calculate the weighted impurity of the child nodes when comparing possible tree splits.
- Treat the number of neighbors and tree depth as choices that affect generalization.
Attempt the questions using the assumptions and notation stated in each problem. For short-answer questions, round only at the final step to the requested precision. Check the provided key after solving and revisit the relevant concept when an answer differs.