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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. If Linear regression model perfectly first i.e., train error is zero, then
2. If two variables are correlated, is it necessary that they have a linear relationship?
3. Which of the following assumptions do we make while deriving linear regression parameters?
1. The true relationship between dependent y and predictor x is linear
2. The model errors are statistically independent
3. The errors are normally distributed with a 0 mean and constant standard deviation
4. The predictor x is non-stochastic and is measured error-free
4. _____which can accept a NumPy RandomState generator or an integer seed.
5. Which of the following is characteristic of best machine learning method ?
6. _____ provides some built-in datasets that can be used for testing purposes.
7. Which of the following can be one of the steps in stacking?
1. Divide the training data into k folds
2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold
3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms
8. The parameter______ allows specifying the percentage of elements to put into the test/training set
9. Support vectors are the data points that lie closest to the decision surface.
10. The cost parameter in the SVM means:
11. Which are two techniques of Machine Learning ?
12. How it's possible to use a different placeholder through the parameter............. .
13. In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
14. What is/are true about ridge regression?1. When lambda is 0, model works like linear regression model2. When lambda is 0, model doesnt work like linear regression model3. When lambda goes to infinity, we get very, very small coefficients approaching 04. When lambda goes to infinity, we get very, very large coefficients approaching infinity
15. Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
16. What does learning exactly mean?
17. For the given weather data, Calculate probability of playing
18. In reinforcement learning, this feedback is usually called as .
19. Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection?
20. Generally, an ensemble method works better, if the individual base models have ____________? Note: Suppose each individual base models have accuracy greater than 50%.
21. When it is necessary to allow the model to develop a generalization ability and avoid a common problem called............. .
22. Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
23. If you remove the non-red circled points from the data, the decision boundary will change?
24. Which of the following is true about Naive Bayes ?
25. The term............. can be freely used, but with the same meaning adopted in physics or system theory.

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