Machine Learning Quiz

Question 1

What is the difference between L1 (Lasso) and L2 (Ridge) regularization?

Accepted Answer

L1 penalizes the absolute magnitude of the coefficients while L2 penalizes the square of the coefficients.

Question 2

What is the type of machine learning algorithms used to draw inferences from labeled data?

Accepted Answer

Supervised Learning.

Question 3

Can we use L1 regularization for feature selection?

Accepted Answer

Yes, because the nature of L1 regularization will lead to sparse coefficients of features. Feature selection can be done by keeping only features with non-zero coefficients.

Question 4

If someone ask you: Out of all the possible positive labels, how many did the model correctly identify? Which metric should you calculate?

Accepted Answer

Recall: true positives over all actual positives, TP / (TP + FN). It is the metric to optimise when a miss is the expensive error, as in fraud or disease screening. You can always buy recall by lowering the threshold, which is why it is quoted next to precision.

Question 5

What is precision?

Accepted Answer

Precision is the number of the correct predictions of the positive class divided by the all the predicted positive class. TP/(TP + FP)

Question 6

High variance can cause an algorithm to overfit? True of False?

Accepted Answer

True.

Question 7

Can we have both L1 and L2 regularization components in a linear model?

Accepted Answer

Yes, it is called Elastic net regularization.

Question 8

Does feature selection tends to increase overfitting?

Accepted Answer

No. It actually could help to reduce overfitting.

Question 9

What is F1-score?

Accepted Answer

Is a measure of a test's accuracy. F1-score is the harmonic mean of the precision and recall.

Question 10

Why is naive Bayes 'naive'?

Accepted Answer

Because it assumes that all of the features in a data set are equally important and independent.

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