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Machine Learning Lab
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Random Forest extends Bagging by adding an additional source of randomization. What is it?
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PCA finds principal components by doing which of the following?
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Linear Discriminant Analysis (LDA) for dimensionality reduction seeks projection vectors w that do which of the following?
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K-Means minimizes which objective function?
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In the EM algorithm for a Gaussian Mixture Model (GMM), what are the E-step and M-step respectively?
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In Bagging with bootstrap sampling, approximately what fraction of training samples are left out (Out-Of-Bag) in any single bootstrap sample? Use the limit of (1 - 1/n)^n as n approaches infinity.
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In AdaBoost, suppose a weak learner achieves a weighted error of epsilon = 0.2. Using alpha = 0.5 * ln((1 - epsilon) / epsilon), what is the learner weight alpha?
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In AdaBoost, after round t, how are sample weights updated for misclassified samples?
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In a lab, you run K-Means for K = 1 to 10 and plot WCSS (inertia) vs K. The plot shows a sharp decrease from K=1 to K=3, then only gradual decline from K=4 onward. What should you conclude?
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In a collaborative filtering recommender system, you apply truncated SVD to a user-item rating matrix: A is approximately U_k * Sigma_k * V_k-transpose. What does each component represent?
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