Assignment 6: Optimizing Model Parameters - Programming for Data Science (Fall 2025)

Quick Facts¶

Create one notebook for this assignment, audits
Export as myst markdown (by installing jupytext which should include frontend features )
Upload (or push) to a branch called assignment6
Open a PR

summary Extend the work you did in assignment 5 by optimizing the model parameters for the (or one of the if you worked alone) model you trained.

Choose your dataset, task, and a model^[1]. It can be any model we have used in class so far. Include a breif description of the task^[2].
Fit the model with default parameters and check the score. Interpret the score in context.^[3]
Choose reasonable model parameter values for your parameter grid by assessing how well the model fit with the default values.
Use grid search to find the best performing model parameters.
Examine the best fit model, how different is it from the default? Score the best fit model on a held out test set.
Examine and interpret the cross validation results. How do they vary in terms of time? Is the performance meaningfully different or just a little?
Try varying the cross validation parameters (eg the number of folds and/or type of cross validation). Does this change your conclusions?

Footnotes¶

if the model you used in Assignment 5 model does not have hyperparameters to optimize, swap in a different model. For example, use LASSO instead of LinearRegression or DecisionTreeClassifier instead of GaussianNaiveBayes`. You should be able to copy your previous training and only change a single line to accomodate for this.
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this can be copied from 5 if applicable or revised if you think it was vague
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again, can be reused, but please do copy it in
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