logo Use CA10RAM to get 10%* Discount.
Order Nowlogo
(5/5)

In this project, we will use the Communities and Crime Unnormalized Data Set available at the UCI Machine Learning Repository

INSTRUCTIONS TO CANDIDATES
ANSWER ALL QUESTIONS

Project Assignment:2

THOROUGHLY READ AND FOLLOW THE PROJECT GUIDELINES. These guidelines contain detailed information about how to structure your project, and how to prepare your written and submissions.

Data Mining Technique(s): We will run experiments using the following techniques:

Pre-processing Techniques: Feature selection, feature creation, dimensionality reduction, noise reduction, attribute discretization, ...

Classification Techniques:

Zero-R (majority class)

One-R

Decision trees: J4.8 in Weka (given that J4.8 is able to handle numeric attributes and missing values directly, make sure to run some experiments with no pre-processing and some experiments with pre-processing, and compare your results); or the decision tree functions in Matlab (see Matlab decision tree demo); or both.

Regression Techniques:

Linear Regression (under "functions" in Weka)

Regression Trees: M5P (under "trees" in Weka)

Model Trees: M5P (under "trees" in Weka)

Dataset(s): In this project, we will use the Communities and Crime Unnormalized Data Set available at the UCI Machine Learning Repository. Convert the dataset to the arff format. The arff header is provided in the dataset webpage. Use the murdPerPop attribute as the target.

For classification, discretize murdPerPop in 3 equal-frequency bins, using unsupervised discretization.

For regression, keep murdPerPop as a continuous attribute.

Run experiments with and without discretizing the predicting attributes, removing attributes that are too "related" to the target (e.g., murders, pop, ...) or that make the trees long (e.g., states), and any other pre-processing and experiments that produce useful and meaningful models.

Performance Metric(s):

Use (1) classification accuracy (in classification tasks), or prediction error (in regression tasks, see note below), (2) size of the tree, and (3) readability of the tree, as separate measures to evaluate the "goodness" of your models.

Note: For regression tasks, use any subset of the following error metrics that you find appropriate: mean-squared error, root mean-squared error, mean absolute error, relative squared error, root relative squared error, relative absolute error, correlation coefficient . An important part of the data mining evaluation in this project is to try to make sense of these performance metrics and to become familiar with them.

Compare each accuracy/error you obtained against those of benchmarking techniques as ZeroR and OneR over the same (sub-)set of data instances you used in the corresponding experiment.

Remember to experiment with pruning of your tree: Experiment with pre- and/or post-prunning of the tree in order to increase the classification accuracy, reduce the prediction error, and/or reduce the size of the tree.

Advanced Topic(s) (20 points): Investigate in more depth (experimentally, theoretically, or both) a topic of your choice that is related to decision trees and that is not covered already in this project. This decision tree-related topic might be something that was described or mentioned in the textbook or in class, or that comes from your own research, or that is related to your interests. Just a few ideas are: The prune function in Matlab; C4.5; C4.5 pruning methods (for trees or for rules); any of the additional tree classifiers in Weka: DecisionStump, LMT RandomForest, RandomTree, REPTree; meta-learning applied to decision trees (see Classifier -> Choose -> meta); an idea from a research paper that you find intriguing; ...

 

 

(5/5)
Attachments:

Related Questions

. Introgramming & Unix Fall 2018, CRN 44882, Oakland University Homework Assignment 6 - Using Arrays and Functions in C

DescriptionIn this final assignment, the students will demonstrate their ability to apply two ma

. The standard path finding involves finding the (shortest) path from an origin to a destination, typically on a map. This is an

Path finding involves finding a path from A to B. Typically we want the path to have certain properties,such as being the shortest or to avoid going t

. Develop a program to emulate a purchase transaction at a retail store. This program will have two classes, a LineItem class and a Transaction class. The LineItem class will represent an individual

Develop a program to emulate a purchase transaction at a retail store. Thisprogram will have two classes, a LineItem class and a Transaction class. Th

. SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of Sea Ports. Here are the classes and their instance variables we wish to define:

1 Project 1 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of

. Project 2 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of Sea Ports. Here are the classes and their instance variables we wish to define:

1 Project 2 Introduction - the SeaPort Project series For this set of projects for the course, we wish to simulate some of the aspects of a number of

Ask This Question To Be Solved By Our ExpertsGet A+ Grade Solution Guaranteed

expert
Um e HaniScience

801 Answers

Hire Me
expert
Muhammad Ali HaiderFinance

632 Answers

Hire Me
expert
Husnain SaeedComputer science

705 Answers

Hire Me
expert
Atharva PatilComputer science

556 Answers

Hire Me
March
January
February
March
April
May
June
July
August
September
October
November
December
2025
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
SunMonTueWedThuFriSat
23
24
25
26
27
28
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
1
2
3
4
5
00:00
00:30
01:00
01:30
02:00
02:30
03:00
03:30
04:00
04:30
05:00
05:30
06:00
06:30
07:00
07:30
08:00
08:30
09:00
09:30
10:00
10:30
11:00
11:30
12:00
12:30
13:00
13:30
14:00
14:30
15:00
15:30
16:00
16:30
17:00
17:30
18:00
18:30
19:00
19:30
20:00
20:30
21:00
21:30
22:00
22:30
23:00
23:30