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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 5

Q1 | MLE estimates are often undesirable because
  • they are biased
  • they have high variance
  • they are not consistent estimators
  • none of the above
Q2 | The difference between the actual Y value and the predicted Y value found using a regression equation is called the
  • slope
  • residual
  • outlier
  • scatter plot
Q3 | Neural networks
  • optimize a convex cost function
  • always output values between 0 and 1
  • can be used for regression as well as classification
  • all of the above
Q4 | Linear Regression is a _______ machine learning algorithm.
  • supervised
  • unsupervised
  • semi-supervised
  • can\t say
Q5 | Which of the following methods/methods do we use to find the best fit line for data in Linear Regression?
  • least square error
  • maximum likelihood
  • logarithmic loss
  • both a and b
Q6 | Which of the following methods do we use to best fit the data in Logistic Regression?
  • least square error
  • maximum likelihood
  • jaccard distance
  • both a and b
Q7 | Lasso can be interpreted as least-squares linear regression where
  • weights are regularized with the l1 norm
  • the weights have a gaussian prior
  • weights are regularized with the l2 norm
  • the solution algorithm is simpler
Q8 | Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
  • auc-roc
  • accuracy
  • logloss
  • mean-squared-error
Q9 | Simple regression assumes a __________ relationship between the input attribute and output attribute.
  • quadratic
  • inverse
  • linear
  • reciprocal
Q10 | In the regression equation Y = 75.65 + 0.50X, the intercept is
  • 0.5
  • 75.65
  • 1
  • indeterminable
Q11 | The selling price of a house depends on many factors. For example, it depends on the number of bedrooms, number of kitchen, number of bathrooms, the year the house was built, and the square footage of the lot. Given these factors, predicting the selling price of the house is an example of ____________ task.
  • binary classification
  • multilabel classification
  • simple linear regression
  • multiple linear regression
Q12 | Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
  • you will add more features
  • you will remove some features
  • all of the above
  • none of the above
Q13 | 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. Now we increase the training set size gradually. As the training set size increases, What do you expect will happen with the mean training error?
  • increase
  • decrease
  • remain constant
  • can’t say
Q14 | 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
Q15 | Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.(i) Models which overfit are more likely to have high bias(ii) Models which overfit are more likely to have low bias(iii) Models which overfit are more likely to have high variance(iv) Models which overfit are more likely to have low variance
  • (i) and (ii)
  • (ii) and (iii)
  • (iii) and (iv)
  • none of these
Q16 | Which of the following indicates the fundamental of least squares?
  • arithmetic mean should be maximized
  • arithmetic mean should be zero
  • arithmetic mean should be neutralized
  • arithmetic mean should be minimized
Q17 | Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is 0.95.
  • relation between the x1 and y is weak
  • relation between the x1 and y is strong
  • relation between the x1 and y is neutral
  • correlation can’t judge the relationship
Q18 | In terms of bias and variance. Which of the following is true when you fit degree 2 polynomial?
  • bias will be high, variance will be high
  • bias will be low, variance will be high
  • bias will be high, variance will be low
  • bias will be low, variance will be low
Q19 | Which of the following statements are true for a design matrix X ∈ Rn×d with d > n? (The rows are n samplepoints and the columns represent d features.)
  • least-squares linear regression computes theweights w = (xtx)−1 xty
  • the sample points are linearly separable
  • x has exactly d − n eigenvectors with eigenvaluezero
  • at least one principal component direction is orthogonal to a hyperplane that contains all the samplepoints
Q20 | Point out the wrong statement.
  • regression through the origin yields an equivalent slope if you center the data first
  • normalizing variables results in the slope being the correlation
  • least squares is not an estimation tool
  • none of the mentioned
Q21 | Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider?
  • you will add more features
  • you will remove some features
  • all of the above
  • none of the above
Q22 | If X and Y in a regression model are totally unrelated,
  • the correlation coefficient would be -1
  • the coefficient of determination would be 0
  • the coefficient of determination would be 1
  • the sse would be 0
Q23 | Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model.(i) Models which overfit are more likely to have high bias(ii) Models which overfit are more likely to have low bias(iii) Models which overfit are more likely to have high variance(iv) Models which overfit are more likely to have low variance
  • (i) and (ii)
  • (ii) and (iii)
  • (iii) and (iv)
  • none of these
Q24 | Which of the following statements are true for a design matrix X ∈ Rn×d with d > n? (The rows are n sample points and the columns represent d features.)
  • least-squares linear regression computes theweights w = (xtx)−1 xty
  • the sample points are linearly separable
  • x has exactly d − n eigenvectors with eigenvaluezero
  • at least one principal component direction is orthogonal to a hyperplane that contains all the samplepoints
Q25 | Problem in multi regression is ?
  • multicollinearity
  • overfitting
  • both multicollinearity & overfitting
  • underfitting