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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 30
Q1 | What is the purpose of performing cross-validation?
- a. To assess the predictive performance of the models
- b. To judge how the trained model performs outside the sample on test data
- c. Both A and B
Q2 | 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 option
Q3 | Which of the following is not supervised learning?
- PCA
- Decision Tree
- Naive Bayesian
- Linerar regression
Q4 | ______can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm.
- Supervised
- Semi-supervised
- Reinforcement
- Clusters
Q5 | In reinforcement learning, this feedback is usually called as___.
- Overfitting
- Overlearning
- Reward
- None of above
Q6 | In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called_____.
- Deep learning
- Machine learning
- Reinforcement learning
- Unsupervised learning
Q7 | there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called _____
- Regression
- Accuracy
- Modelfree
- Scalable
Q8 | ______ showed better performance than other approaches, even without a context-based model
- Machine learning
- Deep learning
- Reinforcement learning
- Supervised learning
Q9 | If two variables are correlated, is it necessary that they have a linear relationship?
- Yes
- No
Q10 | If Linear regression model perfectly first i.e., train error is zero, then _____________________
- Test error is also always zero
- Test error is non zero
- Couldn’t comment on Test error
- Test error is equal to Train error
Q11 | In syntax of linear model lm(formula,data,..), data refers to ______
- Matrix
- Vector
- Array
- List
Q12 | 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 don’t have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate
- 1 and 2
- 1 and 3.
- 2 and 3.
- 1,2 and 3.
Q13 | Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.
- The relationship is symmetric between x and y in both.
- The relationship is not symmetric between x and y in both.
- The relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric.
- The relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric.
Q14 | Let’s say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data.You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset?
- All categories of categorical variable are not present in the test dataset.
- Frequency distribution of categories is different in train as compared to the test dataset.
- Train and Test always have same distribution.
- Both A and B
Q15 | _____which can accept a NumPy RandomState generator or an integer seed.
- make_blobs
- random_state
- test_size
- training_size
Q16 | In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least_____valid options
- 1
- 2
- 3
- 4
Q17 | ______is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
- Removing the whole line
- Creating sub-model to predict those features
- Using an automatic strategy to input them according to the other known values
- All above
Q18 | It's possible to specify if the scaling process must include both mean and standard deviation using the parameters________.
- with_mean=True/False
- with_std=True/False
- Both A & B
- None of the Mentioned
Q19 | Function used for linear regression in R is __________
- lm(formula, data)
- lr(formula, data)
- lrm(formula, data)
- regression.linear(formula, data)
Q20 | In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to __________
- (X-intercept, Slope)
- (Slope, X-Intercept)
- (Y-Intercept, Slope)
- (slope, Y-Intercept)
Q21 | We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
- Bias increases and Variance increases
- Bias decreases and Variance increases
- Bias decreases and Variance decreases
- Bias increases and Variance decreases