Multiple Regression
- The student:
- Minimally computes the RMSE of more than one model
- Discusses how to RMSE relates to relates to the data set in human readable language
- Discusses one claim that may be made about the data based on these multiple RMSEs.
- Supports any numerical claims made with code.
Feature Engineering
- The student:
- Minimally adds 6 new features.
- Removes all non-engineered features.
- Calculates an RMSE and compares it to the earlier RMSEs.
- Comments on independence of features or achieves independence through engineering.
Naive Classification
- The student:
- Minimally provides \(K\)-NN and/or Naive Bayes specialized features.
- Reports Kappa values for more than one method.
- Provides a narrative for the difference in Kappa values.
Improved Classification
- The student:
- Calculates relative frequencies of some categories.
- Derives weights from these categories.
- Trains and new model with novel Kappa value.
- Provides a narrative for the difference in Kappa values.
Ethics
- The student:
- Writes at a college level.
- Does not contradict code samples.