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Date
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CS 580: Introduction to Artificial Intelligence
Project 3: Decision Tree Classifier for Wines
INSTRUCTIONS
•
This Project is considered individual effort and the honor code applies when reviewing the
submission.
•
Submit your solution as
P3_<username>.py
, and your report
P3_<username>.pdf
,
where
<username>
is your Mason account (i.e., your email account).
NOTES:
•
For this project, you are allowed to use the referenced code of the video and make some few
changes.
•
You are NOT allowed to use any predefined tool for the main core of the implementation (e.g.
the decision tree, metric for splitting, etc.) otherwise, the Project is penalized with
-30
points.
•
It is not a wise decision to wait until the last minute to submit your project
, this action may
cause you not to submit your work or submit a wrong file.
•
Once the submission link is closed, we do not accept resubmissions or email submissions, so
it is the responsibility of the student to verify that the files are the correct ones and not
corrupted.
•
Multiple submissions are allowed, and the last attempt is graded.
E
XTRA
C
REDIT
Category
Score
The last attempt was at most 24 hours before due date/time
+5 points
P
ENALTIES
Category
Score
Wrong file name
.py
-3 points
Wrong file name
.pdf
-3 points
Wrong format (it's not a
pdf
file)
-4 points
Wrong template (it's not the
IEEE
article template for conferences)
-10 points
The
.py
implementation is using predefined tools for the decision
tree, metrics, etc.
-30 points
CS580: Introduction to Artificial Intelligence
George Mason University
Prof. Ana Loreto González Hernández loreto@gmu.edu
2/2
Introduction
A
classifier
in
machine learning
is an algorithm that automatically orders or categorizes data into one or
more of a set of classes.
Supervised
classifiers are fed training datasets, from which they learn to classify
data according to predetermined categories.
Implementation
In this project you will implement in Python a decision tree
classifier
for wines. The classifier must show
how the decision tree was built, i.e., the attributes used for the classification and how were splitted. The
attributes are specified in the header of the
wines.cvs
file, where the type of wine in each example is
indicated.
NOTE:
The header was included in the original
wines.cvs
file only to indicate the attributes in
each column,
when implementing your classifier, remove the first line (header) from the wines.cvs
file and overwrite the original file
.
Access the following link to watch the video that explains the code used for this project:
https://www.youtube.com/watch?v=sgQAhG5Q7iY
Deliverables
Submit two files: the
code
P3_<username>.py
and a
report
P3_<username>.pdf
.
After your implementation, run the classifier using the dataset for training. Perform a parameter tuning
to determine values that will allow your classifier
to achieve at least 85% accuracy
. To get full credit on
this aspect, it is not enough to just give the final values of the classifier parameters, but to explain in your
report how the process was carried out to determine the parameter adjustment to obtain that precision,
what values were tested and why you selected those values.
Elaborate a report written in Latex using the IEEE article template for conferences and its format
specifications. Include at least the following:
•
Abstract and keywords
•
Introduction
•
Background
•
Proposed Approach
•
Experimental Results
•
Conclusions
•
References (at least three, including the textbook)
At this point in the course, it is assumed that you are familiar with the information that each section should
contain as a minimum requirement. Keep in mind that omitting basic information on your report may
cause you to get partial credit.
Note
: Review the description of previous projects to remember the basic information that a conference
document must contain.
Your preview ends here
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File
Home
Insert
Draw
Page Layout
Formulas
Data
Review
View
Help
A Share
P Comments
14
fe
A
E
G
H
J
K
L
M
Q
R
1 Semeste Course NumbelCoure NameCredit Hours Letter Grad Grade Points
Directions
Create a spreadsheet that correctly
calculates your GPA - must use formulas for
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2
3
4
To calculate GPA dividing the total amount
5
of grade points earned by the total amount
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6
7
8
average may range from 0.0 to a 4.0.
9
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3
letter grade you earned.
• A = 4
B = 3
C = 2
5
-7
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EMPLOYEE
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