DUE DATE: Thursday Nov 5, 2009.
- Slides: Submit by email by 1:00 pm.
- Written report: Hand in a hardcopy by 2:00 pm.
- Oral Presentation: during class that day.
This assignment consists of two parts:
- A homework part in which you will focus on the construction and/or
pruning of the models.
- A project part in which you will focus on the experimental evaluation
and analysis of the models.
I. Homework Part
[100 points]
In this part of the assignment, we will investigate the pruning techniques
used for regression trees and for model trees.
Consider the dataset below.
This dataset is a small, subsample of the
RED Wine Quality Dataset.
@relation 'sample-winequality-red.arff-weka.filters.unsupervised.attribute.Remove-R1-3,5-8,10-weka.filters.unsupervised.instance.Resample-S1-Z1.0-no-replacement'
@attribute 'residual sugar' numeric
@attribute pH numeric
@attribute alcohol numeric
@attribute quality numeric
@data
2.3,3.52,9.7,5
1.8,3.35,10.1,5
2,3.33,9.5,5
2.6,3.37,10.5,6
3.1,3.17,10.5,5
4,3.36,10.7,6
1.7,3.41,9.5,6
2.9,3.23,12.5,6
3.2,3.56,12.7,6
4.25,3.63,10.7,3
2.2,3.2,9.2,4
2.4,3.13,11.9,7
3.6,3.23,12,7
1.8,3.35,11,7
1.8,3.42,9.5,6
For this homework, we want to predict the quality attribute (prediction target)
from the other predicting attributes
residual sugar,
pH, and
alcohol.
- [5 points]
Build a regression tree for this dataset in Weka using M5P with the
following parameters:
| build regression tree: True
| unpruned: True
| useUnsmoothed: True
| default values for the remaining parameters
|
Record the tree in your report.
- [45 points]
Build a regression tree for this dataset in Weka using M5P with the
following parameters:
| build regression tree: True
| unpruned: False
| useUnsmoothed: True
| default values for the remaining parameters
|
Record the tree in your report [5 points].
Follow the regression tree pruning procedure by hand
(read the corresponding Weka code in detail for this)
so that you can describe in your report each of the steps followed
by the pruning procedure. Include in your report all the
necessary formulas and a description of the calculations done to prune
the regression tree in Part 1 above to obtain
the resulting pruned regression tree of this part [40 points].
- [5 points]
Build a model tree for this dataset in Weka using M5P with the
following parameters:
| build regression tree: False
| unpruned: True
| useUnsmoothed: True
| default values for the remaining parameters
|
Record the tree in your report.
- [45 points]
Build a model tree for this dataset in Weka using M5P with the
following parameters:
| build regression tree: False
| unpruned: False
| useUnsmoothed: True
| default values for the remaining parameters
|
Record the tree in your report [5 points].
Follow the model tree pruning procedure by hand
(read the corresponding Weka code in detail for this)
so that you describe in your report each of the steps followed
by the pruning procedure. Include in your report all the
necessary formulas and a description of the calculations done to prune
the model tree in Part 3 above to obtain
the resulting pruned model tree of this part [40 points].
II. Project Part
[250 points: 50 points for linear regression, 100 points for regression trees,
and 100 points for model trees.
See
Project Guidelines
for the detailed distribution of these points.]
- Project Instructions:
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 oral reports.
- Data Mining Technique(s):
We will run experiment using regression techniques.
You need to use:
- Linear Regression (under "functions" in Weka)
- Regression Trees: M5P (under "trees" in Weka)
- Model Trees: M5P (under "trees" in Weka)
on each dataset.
- Dataset(s):
In this project, we will use the
The
WHITE Wine Quality Dataset.
Use the WHITE wine dataset and the attribute quality as the
prediction target.
After you run experiments predicting this attribute you may, if you wish,
run additional experiments using a different predicting target of your choice.
- Performance Metric(s):
Use the metrics listed in Table 5.8 (page 178) of the textbook
to measure the goodness of your models.
A major part of this project is to try to make sense of these
performance metrics and to become familiar with them.
When comparing the performance of different models, use tables
like Table 5.9 (page 179) of the textbook.