Description
Experiments
Run the following experiments in a Jupyter notebook, performing each action in a code cell and
answering each question in a Markdown cell.
1. Use read_csv() to load and examine each dataset.
2. Use logistic regression to fit() and score() a binary classifier for dataset 1. How
accurate are the model’s predictions?
3. Repeat experiment (2) for dataset 2. How well does it score?
4. Create scatterplots for datasets 1 and 2, plotting points from class 0 with a different color
and marker from points in class 1. What accounts for the discrepancies between
experiments (2) and (3)?
5. Fit and score Gaussian Naive Bayes classifiers for datasets 1 and 2. How well do these
classifiers score compared to logistic regression?
6. Repeat experiment (5) with K-Nearest Neighbor classifiers.
7. Using the second half of the Python code for Figure 9.2 – Simple Gaussian Naive Bayes
Classification from Statistics, Data Mining, and Machine Learning in Astronomy, 2nd
Edition as a guide, plot the decision boundaries for each classifier and dataset. What
differences do you observe?
8. Now repeat experiments (2), (5), (6), and (7) with dataset 3.
Submission
Submit your Jupyter .ipynb notebook file through Canvas before class on the due date. Your
notebook should include the usual identifying information found in a README.TXT file.
If the assignment is completed by a team, only one submission is required. Be certain to identify
the names of all students on your team at the top of the notebook.
