[SOLVED] Qmss 5073 homework 3: midterm review

Part A:
1. Describe the importance of training and test data. Why do we separate data into these subsets?
2. What is k-fold cross validation and what do we use it for?
3. How is k-fold cross validation different from stratified k-fold cross validation?
4. Name the 4 types of supervised learning models that we have learned thus far that are used to predict categorical dependent variables like whether an emailis labeled “spam” or “not spam.”
5. Name the 3 types of supervised learning models that we have learned thus far that are used to predict continuous dependent variables like test scores.
Part B:
1. Import the spam dataset and print the first six rows.
2. Read through the documentation of the original dataset here: http://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.names
(http://archive.ics.uci.edu/ml/machine-learning-databases/spambase/spambase.names) . The dependent variable is “spam” where one indicates that an email isspam and zero otherwise. Which three variables in the dataset do you think will be important predictors in a model of spam? Why?
3. Visualize the univariate distribution of each of the variables in the previous question.
4. Choose one model from Part A Question 4. Split the data into training and test subsets. Build a model with the three variables in the dataset that you think
will be good predictors of “spam”. Run the model and evaluate prediction error using k-fold cross-validation. Describe why you chose any particular
parameters for your model (e.g., if you used KNN how did you decide to choose a specific value for k).
5. Repeat the previous question but with a different model from Part A Question 4.
6. Repeat the previous question but with a different model from Part A Question 4.
7. Repeat the previous question but with a different model from Part A Question 4.
8. Now rerun all 4 models with 3 additional variables that you think will help the prediction accuracy. Did this cause the performance to improve over your
previous models?
9. What is a variable that isn’t available in this dataset but you think could increase your final model’s predictive power if you had it? Why do you think it would
improve your model?
Part A
Part B