Week 9 — Models & Prediction (Oct. 18–22)¶
This week talks more about regression, simulation, and introduces the idea of minimizing a loss function.
🧐 Content Overview¶
This video introduces the week.
This video talks more about simulation as a method for studying statistical techniques, which you are doing in the assignment. I also describe more of NumPy’s random number generation facilities.
You should set random seeds for all work that will need randomness, including train/test splits for evaluating predictors.
🎥 Variance, R², and the Sum of Squares¶
This video provides more detail on explained variance and what the \(R^2\) means.
This video introduces the idea of overfitting: learning too much from the training data so we can’t predict the testing data.
📃 Overfitting Example¶
🎥 Replication, Bias, and Variance¶
📃 Bias-Variance Tradeoff¶
🎥 Optimizing Loss¶
🚩 Week 9 Quiz¶
Take the Week 9 quiz in Blackboard (will be up by end of Saturday).
Since this is the second of two very closely intertwined weeks, there are questions about 📅 Week 8 — Regression (Oct. 11–15) in the quiz ads well.
There are several ways you can practice the material so far:
Practice more regressions with World Bank data
Measure World Bank data predictive accuracy with train-test evaluation and mean squared error
📩 Assignment 4¶
Assignment 4 is due October 24, 2021.