# Applied Machine Learning in Python week2 quiz answers

These solutions are for reference only.

It is recommended that you should solve the assignments amd quizes by yourself honestly then only it makes sense to complete the course.

but if you are stuck in between refer these solutions

make sure you understand the solution

dont just copy paste it

answers are in green colour

------------------------------------------------------------------------------

1。

After training a ridge regression model, you find the 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?

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

-------------------------------------------------------------------

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?

Decrease C and gamma

Increase C and gamma

Increase C, decrease gamma

Decrease C, increase gamma

-------------------------------------------------------------------

3。

Which of the following is an example of multiclass classification?

(Select all that apply)

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.

-------------------------------------------------------------------

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

-------------------------------------------------------------------

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?

Ordinary Least Squares Regression

Lasso Regression

Ridge Regression

Logistic Regression

-------------------------------------------------------------------

6。

Match the plots of SVM margins below to the values of the C

parameter that correspond to them.

1, 0.1, 10

10, 1, 0.1

10, 0.1, 1

0.1, 1, 10

-------------------------------------------------------------------

7。

Use Figures A and B below to answer questions 7, 8, 9, and 10.

Looking at the two figures (Figure A, Figure B), determine which

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:

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

-------------------------------------------------------------------

8。

Looking at Figure A and B, what is a value of alpha that optimizes

the R2 score for the Ridge Model?

3

-------------------------------------------------------------------

9。

Looking at Figure A and B, what is a value of alpha that optimizes

the R2 score for the Lasso Model?

10

-------------------------------------------------------------------

10。

When running a LinearRegression() model with default parameters

on the same data that generated Figures A and B the output

coefficients are:

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 Lasso

Model?

0

-------------------------------------------------------------------

11。

Which of the following is true of cross-validation? (Select all that

apply)

Helps prevent knowledge about the test set from leaking

into the model

Fits multiple models on different splits of the data

Increases generalization ability and computational

complexity

Increases generalization ability and reduces

computational complexity

Removes need for training and test sets