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Machine Learning 101 Practice Test: Logistic Regression
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Quiz on logistic regression, hypothesis representation, decision boundary, cost function and gradient descent, logistic regression for multiple classification, and advanced optimization. Logistic regression is a statistical method that uses math to find relationships between two data factors. It uses these relationships to predict the value of one factor based on the other.  Logistic regression is a predictive analysis that estimates the probability of an event based on a given dataset. The dataset contains both independent variables, or predictors, and their corresponding dependent... Show more
Machine Learning 101 Practice Test: Logistic Regression
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25 Questions

1. y = {0, 1, …, 8}. This problem is divided into ______ binary classification problems.
2. What is the generalized cost function?
3. The output in a logistic regression problem is yes (equivalent to 1 or true). What is its possible value?
4. What is the odds ratio?
5. Let m be the number of training instances. What is the summation of cost function multiplied by to get the gradient descent?
6. In a logistic regression problem, what is a possible output for a new instance?
7. y = 1. How does cost(h(x), y) change with h(x)?
8. The output is whether a person will surely vote or surely not vote or may cast a vote, based on one feature. It is an example of multiclass classification.
9. The output is whether a person will vote or not, based on several features. It is an example of multiclass classification.
10. What function is used for hypothesis representation in logistic regression?
11. Let g be the sigmoid function. Let a = 0. What is the value of g(a)?
12. Who developed conjugate gradient method?
13. h(x) = y. What is the cost (h(x), y)?
14. Threshold value is 0.6. h(x) = 0.3 for a particular instance. What is the value of y?
15. Probability of an event occurring is 0.9. What is odds ratio?
16. Let g be the sigmoid function. Let a = -(infinite). What is the value of g(a)?
17. y = {0, 1, 2, 3, 4, 5, 6, 8}. This problem is divided into ______ binary classification problems.
18. Which is a better algorithm than gradient descent for optimization?
19. h(x) = 1, y = 0. What is the cost (h(x), y)?
20. An artificially intelligent car decreases its speed based on its distance from the car in front of it. Which algorithm is used?
21. Ax = b => [2 2, 3 3][x1, x2] = [1, 2]. Let x0, the initial guess be [1, 1]. What is the residual vector?
22. In the L-BFGS algorithm, what does the letter L stand for?
23. The output of logistic regression is always 0 or 1.
24. The outputs of an image recognition system is {0, 0, 1, 0}. The classes are dog, cat, elephant, and lion. What is the image of, according to our algorithm?
25. Can a cancer detection problem be solved by logistic regression?

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