代写MSBA7027、R编程设计代写

日期：2022-12-24 02:33

MSBA7027 Machine Learning

Homework 2

Due 11:59 pm Dec. 28, 2022

Notes:

- You are required to submit 1) original R Markdown file and 2) a knitted HTML or PDF file via

Moodle. Please provide comments for R code wherever you see appropriate. In general, be as

concise as possible while giving a fully complete answer. Nice formatting of the assignment will

receive extra points.

- Remember that the Class Policy strictly applies to homework. You are encouraged to work in

groups and discuss with fellow students. However, each student has to know how to answer the

questions on her/his own.

- Please allow some buffer time and do not submit homework at the last moment. You will have

points deducted if you submit the above two items late.

Question. Load the dataset from HW2_house_dataset.csv. You will implement some tree-

based methods to predict housing prices. Basic characteristics of the dataset are given as follows:

- Problem type: supervised learning, regression

- Response variable: selling price of houses (in log10 units)

- Data variable name in R: “price”

- Number of features: 17

- Number of observations: 21,613

- Task: use house attributes to predict sale price of a house

Please perform the following tasks:

(1) Set seed

(2) Perform stratified sampling, use 80% as training and 20% as testing. Do not touch the testing data

until the last problem (7).

(3) Perform random forest (RF) on the training data. Find the best tuning parameters and describe

how you find them, and after that report the smallest cross-validated RMSE on the training data.

Which four predictors are the most important? Obtain PDPs for these four predictors, describe

them and provide possible explanations.

(4) Repeat (3) for basic GBM algorithm.

(5) (Optional, completing this part will earn you up to 5 bonus points) Repeat (3) for Xgboost

algorithm.

(6) Are the four most important variables different in (3)-(4)? (or (3)-(5) if you have done (5) )

(7) Among RF and GBM (and Xgboost, if you have done (5) ) with their own best-tuning parameters,

which one has the smallest cross-validated RMSE on the training data? Choose that method, refit

the model with all of the training data, use that model to make prediction on the testing data, report

the RMSE for the testing data. Is the obtained RMSE smaller or larger than the cross-validated

RMSE?

Appendix: Description of Features

price (numeric): sale price (log10 units)

bedrooms (numeric): number of bedrooms

bathrooms (numeric): number of bathrooms

sqft_living (numeric): size of living space

sqft_lot (numeric): size of property

floors (numeric): number of floors

waterfront (numeric): binary indicator for a waterfront view

view (numeric): rating of the quality of the view

condition (factor): condition of the house (poor to very good)

sqft_above (numeric): size of living space above group

sqft_basement (numeric):size of living space below group

yr_built (numeric): year build

year_renovated (numeric): year renovated and, if not renovated, the year built

zip_code (factor): zip code

latitude (numeric): latitude

longitude (numeric): longitude

nn_sqft_living (numeric): size of living space of 15 neighbors

nn_sqft_lot (numeric):size of lot of 15 neighbors