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CMPT459: Data Mining
创建日期：2024-04-02 17:32:31
标签： CMPT459
Data Mining

CMPT459: Data Mining

Assignment 1 [total marks: 100]


The goal of this assignment is to implement a Decision Tree and to test it on a dataset to classify
people into those who earn less than 50k and more than 50k based on their attributes. The adults
dataset consists of 14 features (6 continuous and 8 categorical) and one class label. Provided
data.zip file includes three files:
• data.summary.txt: information about the features
• adult.data.csv: training data
• adult.test.csv: testing data

a) [25 marks] Present a pseudo-code for a simple Decision Tree with error reduction pruning:
i) The information gain is used as a split criterion. [5 marks]
ii) The tree is grown deep, i.e. it is grown until all training examples corresponding to
a leaf node belong to the same class. [5 marks]
iii) Works on categorical data. [5 marks]
iv) Works on numerical data. [5 marks]
v) Error reduction pruning using validation data. [5 marks]

b) [75 marks] Implement your pseudo-code using Python. Your implementation should
include the following functions (method signatures might be different based on the specific
needs of your implementation):

● grow(dataset) -> tree: grows a deep tree on the given dataset and returns the tree
object. [20 marks]
● prune(dataset, tree) -> tree: accepts a tree object and prunes it using the validation
dataset. Returns the pruned tree. [15 marks]
● test(dataset, tree) -> accuracy: returns the accuracy of the given tree on the given
dataset. [10 marks]

You should use the above methods to complete the following tasks:

1. Train and evaluate on the dataset adult.data.csv using 5-fold-cross-validation. Each
time you grow a tree, you need to prune it before evaluation. You can leave 10% of
the training data as validation data. Report the average accuracy. [15 marks]
2. Train one final tree on the dataset adult.data.csv and use it to predict samples in
adult.test.csv and save outputs in a csv file. [10 marks]
3. You need to properly handle missing values. Explain briefly how you did that. [5
marks]

[IMPORTANT] Submit a file [student-id].zip which includes the following:
● A file report.pdf with the pseudocode and your answers for tasks 1. and 3.
● One and only one .py file which includes all your implemented functions and classes.
● A file predictions.csv with the predictions for the test data.
● A requirements.txt file, including all the required packages to run your code.
● data/ directory with the datasets.
Running the python command on your .py file should reproduce all your reported results. You
need to use relative paths for accessing your data through your code to make it runnable on all
machines.

Deadline: The deadline is 23:59 pm on Feb 11th. We accept late submissions up to 24 hours late
but deduct 10% of the marks. You will lose all the marks for submissions after that.

Libraries: You can use libraries including math, numpy, scipy, random, etc. You MUST provide
YOUR OWN implementation for the information gain calculation, decision tree growing, pruning
and the methods to perform 5-fold-cross validation and predictions. These MUST be implemented
from scratch i.e. not using scikit-learn libraries. You will be marked on the correctness of your
implementation.
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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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