Machine Learning Set 5

Q1 | MLE estimates are often undesirable because

Q2 | The difference between the actual Y value and the predicted Y value found using a regression equation is called the

Q3 | Neural networks

Q4 | Linear Regression is a _______ machine learning algorithm.

Q5 | Which of the following methods/methods do we use to find the best fit line for data in Linear Regression?

Q6 | Which of the following methods do we use to best fit the data in Logistic Regression?

Q7 | Lasso can be interpreted as least-squares linear regression where

Q8 | Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

Q9 | Simple regression assumes a __________ relationship between the input attribute and output attribute.

Q10 | In the regression equation Y = 75.65 + 0.50X, the intercept is

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.

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?

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?

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?

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

Q16 | Which of the following indicates the fundamental of least squares?

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.

Q18 | In terms of bias and variance. Which of the following is true when you fit degree 2 polynomial?

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.)

Q20 | Point out the wrong statement.

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?

Q22 | If X and Y in a regression model are totally unrelated,

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

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.)

Q25 | Problem in multi regression is ?