CS 4320: Machine Learning

Assignment: Decision Tree Classification

Use the Heart Attack data set at Kaggle. Create a decision tree classification model (such as sklearn.tree.DecisionTreeClassifier), to obtain the best F1 score possible.

It is expected that you will use the Titantic decision tree source code as a starting point for your code development.

Create a report that includes the data exploration plots and analysis, the learning hyper parameters you tried, the F1 score for these attempts, and the final hyper parameters and F1 score found.

Include a discussion on the effects of the decision tree hyper parameters.

Report the best trained model’s F1 score on the test data.

Report on the suitability of your model for production on this system.

Required Steps

Download your data.
Explore and analyze your data.
Split the data 80%/20%, for training/testing.
Write (or modify) a Python program using sklearn to process and fit the training data to decision tree models with various hyper parameters.
AFTER finding your best fit model, measure the model’s F1 score on your test data.
Create a report with the contents mentioned above.
Commit and push your code in the git repository.
Submit the report (as PDF) to Canvas.

Last Updated 01/16/2023