University of Massachusetts, Amherst COMPSCI 589 Midterm Solution-VERIFIED BY EXPERTS 2021

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1 True/False [20 points] 1.1 [2 points] KNN is an unsupervised learning algorithm. SOLUTION: False - the input is (x; y) pairs = supervised learning 1.2 [2 points] A classifier is less likely to ov... erfit when trained on less training data. SOLUTION: False - overfitting is usually the result of variance which decreases with more data 1.3 [2 points] Given a housing dataset, we want to predict the housing prices given the attributes of the house. Logistic regression could be an appropriate model for the task. SOLUTION: False { logistic regression is a classifier and we want a continuous real value 1.4 [2 points] It usually takes a longer time to train a KNN than a SVM. SOLUTION: False { knn essentially requires no training whereas SVM = convex opt. 1.5 [2 points] Linear discriminant analysis models the feature vectors of each class as a multivariate Gaussian, sharing the covariance matrix between classes. SOLUTION: True { its the definition of LDA 1.6 [2 points] Moving in the direction of the gradient always decreases the function’s value. SOLUTION: False { the gradient points in the direction of stepest ascent 1.7 [2 points] Linear discriminant analysis is an example of a discriminative classifier. SOLUTION: False { it models P(XjY ) as a gaussian and hence is generative 1.8 [2 points] The algorithm we use to fit logistic regression explicitly minimizes the number of errors on the training set. SOLUTION: False { we are minimizing an upper bound on the training error 1.9 [2 points] The theory for agnostic learning only applies if the hypothesis space contains the true labeling function (aka "concept"). SOLUTION: False { PAC learning makes this assumption, but agnostic learning removes it 1.10 [2 points] When we fit a random forest, each decision tree is allowed to consider all available features. SOLUTION: False { each tree uses a random subset of the feature [Show More]

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