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Machine Learning Lab
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You apply PCA to reduce 100 features to 10 components before training an SVM. Your test accuracy improves. What is the most likely reason?
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Which statement correctly distinguishes Gradient Boosting from AdaBoost?
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Which statement best contrasts LDA and PCA?
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Which statement about EM algorithm convergence is true?
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Which of the following is a key limitation of standard K-Means clustering?
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Which of the following best describes the Bagging (Bootstrap Aggregating) technique?
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Which is a key limitation of standard PCA?
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Which describes the correct sequence of steps in the K-Means algorithm?
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Unlike Bagging which primarily reduces variance, Boosting primarily reduces which component of error, and what is a key risk?
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The Eckart-Young theorem states that the best rank-k approximation to matrix A in Frobenius norm is obtained by which of the following?
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