# Machine Learning Set 29

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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 29

Q1 | Which of the following statements about Naive Bayes is incorrect?
Q2 | The SVM’s are less effective when:
Q3 | If there is only a discrete number of possible outcomes called _____.
Q4 | Some people are using the term ___ instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
Q5 | The term _____ can be freely used, but with the same meaning adopted in physics or system theory.
Q6 | Common deep learning applications / problems can also be solved using____
Q7 | what is the function of ‘Unsupervised Learning’?
Q8 | What are the two methods used for the calibration in Supervised Learning?
Q9 | Let’s say, a “Linear regression” model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
Q10 | In a linear regression problem, we are using “R-squared” to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model.Which of the following option is true?
Q11 | Suppose we fit “Lasso Regression” to a data set, which has 100 features (X1,X2…X100).  Now, we rescale one of these feature by multiplying with 10 (say that feature is X1),  and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct?
Q12 | Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection?
Q13 | Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R-Squared increases and Adjusted R-squared decreases3. R-Squared decreases and Adjusted R-squared decreases4. R-Squared decreases and Adjusted R-squared increases
Q14 | 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
Q15 |  If two variables are correlated, is it necessary that they have a linear relationship?
Q16 | Which of the following option is true regarding “Regression” and “Correlation” ?Note: y is dependent variable and x is independent variable.
Q17 | Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it’s hyper parameter.What would happen when you use very large value of C(C->infinity)?
Q18 | SVM can solve linear and non-linear problems
Q19 | The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space(N — the number of features) that distinctly classifies the data points.
Q20 | Hyperplanes are _____________boundaries that help classify the data points.
Q21 | The _____of the hyperplane depends upon the number of features.
Q22 | Hyperplanes are decision boundaries that help classify the data points.
Q23 | SVM algorithms use a set of mathematical functions that are defined as the kernel.
Q24 | In SVR we try to fit the error within a certain threshold.