Why do we divide the data into train and test data ? To reduce the noise, we train the model twice Train data is more reliable then test One is for building a classifier, one is to measure performance of the classifier the data is so big that we need to divide it Which one is unsupervised learning algorithm decision tree naive bayes classifier K-means k nearest neighbor