---
title: "Homework #7: Stacking and Boosting" 
author: "**Your Name Here**"
format: ds6030hw-html
---



```{r config, include=FALSE}
# Set global configurations and settings here
knitr::opts_chunk$set()                 # set global chunk options
ggplot2::theme_set(ggplot2::theme_bw()) # set ggplot2 theme
```


# Stacking for Kaggle

You are to make at least one official entry in the [House Prices: Advanced Regression Techniques](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview) Kaggle contest **using stacking or model averaging**; at least one component model must be a boosting model. 

- You will need to register in Kaggle (its free)
- Read the details of the contest. Understand the data and evaluation function.
- Make at least one submission that uses **stacking or model averaging**. 
- If you get a score on the public leaderboard of $\text{RMSE}<0.50$ (note RMSE is calculated on the log scale), you receive full credit, otherwise, you'll lose 10 points. 
    - I'll allow [teaming](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/team). Each team member can produce one component model and then use stacking or model averaging to combine predictions. 
    - You don't need to team, but must still combine multiple models. At least one of the component models should be boosting. 
- Each person submit the following in Canvas:
    - Code (if teaming, your code and the shared stacking code)
    - kaggle name (or team name) so we can ensure you had a valid submission. 
    - your score and current ranking on the kaggle leaderboard
- Top 5 scores get 2 bonus points
    - Teams will split their bonus points among team members




Note: Check out the [Kaggle notebooks](https://www.kaggle.com/docs/notebooks) which let you make submissions directly from the notebook. Its very similar to using Rivanna's OnDemand in that you can make a RMarkdown/Jupyter notebook or R/Python scrips that run on the cloud. Free CPU (4 cores, 30GB RAM) - amazing! Let your laptops cool off after all their hard work this semester.