2/20/26
- New paper, uses feature selection and feature scaling
- Feature selection helps model perform better + lowers the cost
- Uses a binary classification tabular dataset, fit the entire set of features
- Some algorithm to select subset of features
- They also do PCA w/ reducing the dimension
- Use selected features in ML models
- Read the paper, understand the paper, and ask questions
- Try to find a dataset, only requirements is that project should not be done before
- For each dataset, let’s create a table with # of features and a categorial response variable
- Considerable amount of features (40-50)
- Maybe take like a couple weeks, if I get something, meeting next Friday at 10:30
2/27/26
- Try to find a response variable
- Then do feature selection and feature scaling
- Meet next Friday + try to finalize the dataset
3/6/26
- Get the entire dataset in one place
- Response variable can be like levels using the rates
3/27/26
- Scale data that is not a percentage