# Week 9 — Models & Prediction

Activities:

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

## 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.

### Resources

## Overfitting

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.

### Resources

Further reading: Lecture 12: Bias-Variance Tradeoff.

## Optimizing Loss

## Loss-Based Regression Notebook

Read the minimization regression notebook notebook.

## 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 25, 2020**.