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Machine Learning (ML) Practice Test Questions
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Machine learning (ML) is a branch of artificial intelligence that leverages data to improve computer performance by giving machines the ability to "learn", or improve performance — based on the data.

There are four basic approaches to machine learning: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.

Machine Learning (ML) Practice Test Questions
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25 Questions

1. Which of the following is an application of NN (Neural Network)?
2. Which of the following is an example of a deterministic algorithm?
3. _______produce sparse matrices of real numbers that can be fed into any machine learning model.
4. If two variables are correlated, is it necessary that they have a linear relationship?
5. Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation?
6. What are the different Algorithm techniques in Machine Learning?
7. Common deep learning applications include
8. When it is necessary to allow the model to develop a generalization ability and avoid a common problem called............. .
9. The main disadvantage of maximum likelihood methods is that they are _____
10. We can also compute the coefficient of linear regression with the help of an analytical method called Normal Equation. Which of the following is/are true about Normal Equation?1. We dont have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
11. In which neural net architecture, does weight sharing occur?
12. We usually use feature normalization before using the Gaussian k
13. What is ‘Overfitting’ in Machine learning?
14. Which of the following statement is true about outliers in Linear regression?
15. How does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same1. In case of fewer observations, it is easy to overfit the data.2. In case of fewer observations, it is hard to overfit the data.3. In case of more observations, it is easy to overfit the data.4. In case of more observations, it is hard to overfit the data.
16. scikit-learn also provides functions for creating dummy datasets from scratch:
17. Reinforcement learning is particularly efficient when _____________............. .
18. A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 10 and 30 respectively. What will be the output?
19. In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to _____________
20. In SVR we try to fit the error within a certain threshold.
21. Supervised learning and unsupervised clustering both require at least one
22. Function used for linear regression in R is
23. Which of the following can act as possible termination conditions in K-Means?
1. For a fixed number of iterations.
2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.
3. Centroids do not change between successive iterations.
4. Terminate when RSS falls below a threshold.
24. Which of the following sentence is FALSE regarding regression?
25. True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model.

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