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Module 2 Quiz
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Module 2 Quiz
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Module 2 quiz
1.
Question 1
After training a ridge regression model, you find that the training and test set accuracies are 0.98 and 0.54 respectively. Which of the following would be the best choice for the next ridge regression model you train? b
1 point
You are overfitting, the next model trained should have a lower value for alpha
You are overfitting, the next model trained should have a higher value for alpha
You are underfitting, the next model trained should have a lower value for alpha
You are underfitting, the next model trained should have a higher value for alpha
Answer 1 - 'b' - You are overfitting, the next model trained should have a higher value for alpha
2.
Question 2
After training a Radial Basis Function (RBF) kernel SVM, you decide to increase the influence of each training point and to simplify the decision surface. Which of the following would be the best choice for the next RBF SVM you train?c
1 point
Decrease C and gamma
Increase C and gamma
Increase C, decrease gamma
Decrease C, increase gamma
Answer 2 - 'c' - Increase C, decrease gamma
3.
Question 3
Which of the following is an example of multiclass classification? (Select all that apply)a,b
1 point
Classify a set of fruits as apples, oranges, bananas, or lemons
Predict whether an article is relevant to one or more topics (e.g. sports, politics, finance, science)
Predicting both the rating and profit of soon to be released movie
Classify a voice recording as an authorized user or not an authorized user.
Answer 3 - 'ab' - Classify a set of fruits as apples, oranges, bananas, or lemons
Predict whether an article is relevant to one or more topics (e.g. sports, politics, finance, science)
4.
Question 4
Looking at the plot below which shows accuracy scores for different values of a regularization parameter lambda, what value of lambda is the best choice for generalization?10
Answer 4 - 10
5.
Question 5
Suppose you are interested in finding a parsimonious model (the model that accomplishes the desired level of prediction with as few predictor variables as possible) to predict housing prices. Which of the following would be the best choice?a
1 point
Lasso Regression
Logistic Regression
Ordinary Least Squares Regression
Ridge Regression
Answer 5 - 'a' - Logistic Regression
6.
Question 6
Match the plots of SVM margins below to the values of the C parameter that correspond to them.b
1 point
10, 1, 0.1
0.1, 1, 10
10, 0.1, 1
1, 0.1, 10
Answer 6 - 'b' - 0.1, 1, 10
7.
Question 7
Use Figures A and B below to answer questions 7, 8, 9, and 10.a
Looking at the two figures (Figure A, Figure B), determine which linear model each figure corresponds to:
1 point
Figure A: Ridge Regression, Figure B: Lasso Regression
Figure A: Lasso Regression, Figure B: Ridge Regression
Figure A: Ordinary Least Squares Regression, Figure B: Ridge Regression
Figure A: Ridge Regression, Figure B: Ordinary Least Squares Regression
Figure A: Ordinary Least Squares Regression, Figure B: Lasso Regression
Figure A: Lasso Regression, Figure B: Ordinary Least Squares Regression
Answer 7 - 'a' - Figure A: Ridge Regression, Figure B: Lasso Regression
8.
Question 8
Looking at Figure A and B, what is a value of alpha that optimizes the R2 score for the Ridge Model?5
Answer 8 - 5
9.
Question 9
Looking at Figure A and B, what is a value of alpha that optimizes the R2 score for the Lasso Model?10
Answer 9 - 10
Question 10
When running a LinearRegression() model with default parameters on the same data that generated Figures A and B the output coefficients are:10
Coef 0 -19.5
Coef 1 48.8
Coef 2 9.7
Coef 3 24.6
Coef 4 13.2
Coef 5 5.1
For what value of Coef 3 is R2 score maximized for the Ridge Model?
Answer 10 - 10
11.
Question 11
Which of the following is true of cross-validation? (Select all that apply)bde
1 point
Increases generalization ability and reduces computational complexity
Fits multiple models on different splits of the data
Removes need for training and test sets
Helps prevent knowledge about the test set from leaking into the model
Increases generalization ability and computational complexity
Answer 11 - 'bde'
Fits multiple models on different splits of the data
Helps prevent knowledge about the test set from leaking into the model
Increases generalization ability and computational complexity