※ 引述《hometoofar (家太远了)》之铭言：
: ※ 引述《rafan (@[email protected]"""")》之铭言：
: : Homework 1 is using k-nearest neighbor method, not SVM.
: : Just forget it.The dataset is a multilabel one, i.e.,
: : one instance can have more than one target value (class
: : labels).
: : Since kNN is original designed for one target value,
: : it is up to you that how to apply it on multilabel
: : dataset.
: Since we are using kNN here, does that mean we don't need to train it? We just
: use the training data and kNN to predict what class label should a test entry
: have and compare it to the true class label? Then we compare different values
: of k and see which k would give the best result?
I think you should use cross validation tecnique
taught in class to decide k. After have a k, you
use the whole training data to predict the testing
data.