# Applied Machine Learning in Python Module 2 Quiz Answer

Hello Friends in this article i am gone to share Applied Machine Learning in Python Coursera Module 2 Quiz Answers with you..

## Applied Machine Learning in Python Quiz Answer

Also visit this link:ย  Applied Machine Learning in Python Module 1 Quiz Answer

Question 1)
After training a ridge regression model, you find the the training and test setย accuracies are 0.98 and 0.54 respectively. Which of the following would be theย best choice for the next ridge regression model you train?
• 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
Question 2)
After training a Radial Basis Function (RBF) kernel SVM, you decideย to increase the influence of each training point and to simplify theย decision surface. Which of the following would be the best choiceย for the next RBF SVM you train?
• Decrease C and gamma
• Increase C and gamma
• Increase C, decrease gamma
• Decrease C, increase gamma
Question 3)
Which of the following is an example of multiclass classification?ย (Select all that apply)
• Classify a set of fruits as apples, oranges, bananas, orย lemons
• Predict whether an article is relevant to one or moreย topics (e.g. sports, politics, finance, science)
• Predicting both the rating and profit of soon to beย released movie
• Classify a voice recording as an authorized user or not anย authorized user.
Question 4)
Looking at the plot below which shows accuracy scores forย different values of a regularization parameter lambda, what valueย of lambda is the best choice for generalization?
• 10
Question 5)
Suppose you are interested in finding a parsimonious model (theย model that accomplishes the desired level of prediction with as fewย predictor variables as possible) to predict housing prices. Which ofย the following would be the best choice?
• Ordinary Least Squares Regression
• Lasso Regression
• Ridge Regression
• Logistic Regression
Question 6)
Match the plots of SVM margins below to the values of the Cย parameter that correspond to them
• 1, 0.1, 10
• 10, 1, 0.1
• 10, 0.1, 1
• 0.1, 1, 10
Question 7)
Use Figures A and B below to answer questions 7, 8, 9, and 10.
Question 8)
Looking at the two figures (Figure A, Figure B), determine whichย linear model each figure corresponds to:
• Figure A: Ridge Regression, Figure B: Lasso Regression
• Figure A: Lasso Regression, Figure B: Ridge Regression
• Figure A: Ordinary Least Squares Regression, Figure B:ย Ridge Regression
• Figure A: Ridge Regression, Figure B: Ordinary Leastย Squares Regression
• Figure A: Ordinary Least Squares Regression, Figure B:ย Lasso Regression
• Figure A: Lasso Regression, Figure B: Ordinary Leastย Squares Regression
Question 8)
Looking at Figure A and B, what is a value of alpha that optimizesย the R2 score for the Ridge Model?
• 3
Question 9)
Looking at Figure A and B, what is a value of alpha that optimizesย the R2 score for the Lasso Model?
• 10
Question 10)
When running a LinearRegression() model with default parametersย on the same data that generated Figures A and B the outputย coefficients are:
For what value of Coef 3 is R2 score maximized for the Lasso Model?
• 0

Question 11)
Which of the following is true of cross-validation? (Select all that apply)
• Helps prevent knowledge about the test set from leakingย into the model
• Fits multiple models on different splits of the dataย
• Increases generalization ability and computationalย complexity
• Increases generalization ability and reducesย computational complexity
• Removes need for training and test sets