Machine Learning

1.

The most widely used metrics and tools to assess a classification model are:

Confusion matrix Cost-sensitive accuracy Area under the ROC curve All of the above

2.

Which of the following is a good test dataset characteristic?

Large enough to yield meaningful results Is representative of the dataset as a whole Both A and B None of the above

3.

What is the purpose of performing cross-validation?

To assess the predictive performance of the models To judge how the trained model performs outside the sample on test data Both A and B None of the above

4.

Why is second order differencing in time series needed?

To remove stationarity To find the maxima or minima at the local point Both A and B None of the above

5.

Suppose you have trained a logistic regression classifier and it outputs a new example x with a prediction ho(x) = 0.2. This means

Our estimate for P(y=1 | x)

Our estimate for P(y=0 | x)

Our estimate for P(y=1 | x)

Our estimate for P(y=0 | x)

6.

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?

Rather than using the current value of a, use a larger value of a (say a=1.0)

Rather than using the current value of a, use a smaller value of a (say a=0.1)

a=0.3 is an effective choice of learning rate

None of the above

7.

How can you prevent a clustering algorithm from getting stuck in bad local optima?

Set the same seed value for each run
Use multiple random initializations Both A and B None of the above

8.

Which of the following techniques can be used for normalization in text mining?

Stemming Lemmatization Stop Word Removal Both A and B

9.

What is pca.components_ in Sklearn?

Set of all eigen vectors for the projection space Matrix of principal components Result of the multiplication matrix None of the above

10.

Which of the following is true about Naive Bayes ?

Assumes that all the features in a dataset are equally important Assumes that all the features in a dataset are independent Both A and B None of the above

Machine Learning

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Congratulations

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