# Week 9 — Models & Prediction (10/17–21)#

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

## 🧐 Content Overview#

Element Length

🎥 More Regression

3m7s

🎥 Simulation

14m48s

📃 Random Numbers

1044 words

🎥 Variance and Sums of Squares

5m59s

🎥 Overfitting

10m31s

📃 Example of overfitting and underfitting in machine learning

1180 words

🎥 Bias-Variance

9m16s

3000 words

🎥 Optimizing Loss

15m23s

This week has 0h59m of video and 5224 words of assigned readings. This week’s videos are available in a Panopto folder.

## 🎥 Introduction#

This video introduces the week.

## 🎥 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 NumPy’s random number generation facilities.

Tip

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.

## 🎥 Overfitting#

This video introduces the idea of overfitting: learning too much from the training data so we can’t predict the testing data.

## 🚩 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 📅 Week 8 — Regression (10/10–14) in the quiz ads well.

## ✅ 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

## 📩 Assignment 4#

Assignment 4 is due October 24, 2021.