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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?
Q2 | Which of the following is true about Naive Bayes ?
Q3 | Which of the following is not supervised learning?
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.
Q5 | In reinforcement learning, this feedback is usually called as___.
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_____.
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 _____
Q8 | ______ showed better performance than other approaches, even without a context-based model
Q9 |  If two variables are correlated, is it necessary that they have a linear relationship?
Q10 | If Linear regression model perfectly first i.e., train error is zero, then _____________________
Q11 | In syntax of linear model lm(formula,data,..), data refers to ______
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
Q13 | Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.
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?
Q15 | _____which can accept a NumPy RandomState generator or an integer seed.
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
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.
Q18 | It's possible to specify if the scaling process must include both mean and standard deviation using the parameters________.
Q19 |  Function used for linear regression in R is __________
Q20 | In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to __________
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?