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CSC597/CSC687 Topics in Computer Science
创建日期：2024-05-08 17:06:20
标签： CSC597
Topics in Computer Science

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CSC597/CSC687 Topics in Computer Science

Assignment 

For this assignment two datasets will be used: the Parkinson’s UPDRS dataset and the
Wisconsin breast cancer dataset, from the UCI Machine Learning Repository. The datasets
are the same ones that were used for the previous assignments and they are available for
download under the “Assignments” section.
• Each dataset has two files associated to it: a “data” file, which contains the actual
values for the different variables, and a “names” file, which contains a description about
the dataset.
• The Parkinson’s UPDRS dataset will be used for regression methods and includes two
possible output variables: motor_UPDRS and total_UPDRS. These should not be
included as part of the input variables.
• The Wisconsin breast cancer dataset contains one output variable, the class, and will
be thus used for classification techniques.

Some useful notes involving missing data:
• Data may need to be cleaned before applying any statistical learning algorithm. Given
that sample size of the datasets is large enough, the rows with missing data should be
removed.
• When the data is first loaded into R, we can indicate how we code the missing values.
• Missing values can be inspected by using the is.na() function.

This assignment will cover tree-based and ensemble methods. Divide the datasets into 80%
for training and 20% for testing.

1. Regression problem: build regression models to predict total_UPDRS (reminder: do not
include motor_UPDRS as input to the model) and measure their performance utilizing the
test set.
a. Build a regression model using bagging (m=p).
i. How does the model perform?
b. Build a regression model using random forests (m=!).
i. How does the model perform?
c. Build a regression model using boosting.
i. How does the model perform?
d. Compare and comment on the error obtained with each approach. Which model
seems to perform the best?
2. Classification problem: build classification models to predict the class variable and measure
their performance utilizing the test set.
a. Build a classification model using decision trees.
i. How does the model perform?
b. Prune the tree obtained in “a”.
i. Use cross-validation to determine the optimal level of tree complexity.
ii. How does the model perform?

Graduate students should address all the questions, whereas undergraduate students are only
required to address questions 1.b, 1.c and 2.a.
案例分类
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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