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+SYS 6018 | Spring 2024 | University of Virginia
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+Problem 1: Stacking for Kaggle
+You are to make at least one official entry in the House Prices: Advanced Regression Techniques Kaggle contest using stacking or model averaging; at least one component model must be a boosting model.
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+- 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.
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+- I’ll allow teaming. 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.
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+- Each person submit the following in Canvas:
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+- 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
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+- Top 5 scores get 2 bonus points
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+- Teams will split their bonus points among team members
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+---
+title: "Homework #7: Stacking and Boosting"
+author: "**Your Name Here**"
+format: sys6018hw-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
+```
+
+# Problem 1: 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
+
+
+::: {.callout-note title="Solution"}
+
+Add your code here.
+
+:::
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