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Week 9 — Models & Prediction


This week's videos are also available in a Panopto playlist.


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 Simulation

Overfitting Example

Read Example of overfitting and underfitting in machine learning.

Week 9 Quiz

Take the Week 9 quiz in Blackboard (will be up by end of Saturday).

Replication, Bias, and Variance

Bias-Variance Tradeoff

Read Understanding the Bias-Variance Tradeoff.


Further reading: Lecture 12: Bias-Variance Tradeoff.

Optimizing Loss

Loss-Based Regression Notebook

Read the minimization regression notebook notebook.


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 25, 2020.