Week 9 โ€” Models & Prediction (10/17โ€“21)#

This week talks more about regression, simulation, and introduces the idea of minimizing a loss function.

๐Ÿง Content Overview#

๐ŸŽฅ Introduction#

This video introduces the week.

๐ŸŽฅ Simulation#

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 NumPyโ€™s random number generation facilities.


You should set random seeds for all work that will need randomness, including train/test splits for evaluating predictors.

๐Ÿ““ Random Numbers#

Read the Random Numbers notebook.

๐ŸŽฅ Variance, Rยฒ, and the Sum of Squares#

This video provides more detail on explained variance and what the \(R^2\) means.


๐ŸŽฅ Overfitting#

This video introduces the idea of overfitting: learning too much from the training data so we canโ€™t predict the testing data.

๐Ÿ““ Overfitting Simulation#

๐Ÿ“ƒ Overfitting Example#

Read Example of overfitting and underfitting in machine learning.

๐ŸŽฅ Replication, Bias, and Variance#

๐Ÿ“ƒ Bias-Variance Tradeoff#

Read Understanding the Bias-Variance Tradeoff.


Further reading: Lecture 12: Bias-Variance Tradeoff.

๐ŸŽฅ Optimizing Loss#

๐Ÿšฉ Week 9 Quiz#

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

Since this is the second of two very closely intertwined weeks, there are questions about ๐Ÿ“…ย Week 8 โ€” Regression (10/10โ€“14) in the quiz ads well.

โœ… Practice#

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

๐Ÿ“ฉ Assignment 4#

Assignment 4 is due October 24, 2021.