Applied Machine Learning in Python
week 2 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
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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
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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
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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.
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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
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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
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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
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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
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
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