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CSCC11 – Introduction to Machine Learning, Winter 2021, Assignment 3
B. Chan, Z. Zhang, D. Fleet
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Answer The Following Questions:

Visualization:
1. Do you expect logistic regression to perform well on generic_1? Why?

What if we apply the feature map defined in Equation (2) on the assignment handout?

2. Do you expect logistic regression to perform well on generic_2? Why?

What if we apply the feature map defined in Equation (2) on the assignment handout?

3. Do you expect logistic regression to perform well on generic_3? Why?

4. Why can’t we directly visualize the wine dataset? What are some ways to visualize it?

Analysis:
1. Generic Dataset 1: Run logistic regression without regularization and without feature map.
Did you run into any numerical errors? If so, why do you think this is the case?

Now, run logistic regression with regularization. What happens?
What are the train and test accuracies?

2. Generic Dataset 2: Run logistic regression without regularization and without feature map.
What are the train and test accuracies?

Run it with feature map now, did the performance get better? Why do you think that is the case?

3. Generic Dataset 3: Run logistic regression without regularization and without feature map.
What are the train and test accuracies?

What if we run it with feature map?

4. What are the training and validation accuracies for the wine dataset?