This Quiz contains totally 10 Questions each carry 1 point for you.
1. Which of the following techniques can be used for normalization in text mining?
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2. What is pca.components_ in Sklearn?
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3. How can you prevent a clustering algorithm from getting stuck in bad local optima?
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4. In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes
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5. Which of the following is true about Naive Bayes ?
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6. Which of the following is a reasonable way to select the number of principal components "k"?
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7. Which of the following statements about regularization is not correct?
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8. What is a sentence parser typically used for?
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9. Suppose you have trained a logistic regression classifier and it outputs a new example x with a prediction ho(x) = 0.2. This means
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10. You run gradient descent for 15 iterations with a=0.3 and compute J(theta) after each iteration. You find that the value of J(Theta) decreases quickly and then levels off. Based on this, which of the following conclusions seems most plausible?
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