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=Third Homework, [[ECE662]] Spring 2012=
 
=Third Homework, [[ECE662]] Spring 2012=
−
Email code to your instructor before 11:59pm, Friday April 27, 2012. Report due before 11:59pm, Monday April 30, in [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=mboutin&assn=true your instructor's Rhea dropbox]. '''Make sure to drop it in the correct homework folder!!!!'''. It is the one at the very bottom of the page.
+
*Drop test set output, predicted accuracy, and proposed nickname into [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=drugeles&assn=true Daniel's drop box] before 11:59pm, Friday April 27, 2012.  
 +
*Report due before 11:59pm, Monday April 30, in [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=mboutin&assn=true your instructor's Rhea dropbox]. '''Make sure to drop it in the correct homework folder!!!!'''. It is the somewhere in the middle of the page.
 
----
 
----
 
==Automatic Pattern Recognition Contest!==
 
==Automatic Pattern Recognition Contest!==
 
An anonymous company has agreed to share real data with us, so we are going to have a little contest using this data! The data comes from a five-class classification problem using 13 features. We are looking for the student who will design the most accurate classifier using this data.
 
An anonymous company has agreed to share real data with us, so we are going to have a little contest using this data! The data comes from a five-class classification problem using 13 features. We are looking for the student who will design the most accurate classifier using this data.
  
−
The [[training_data_HW3_ECE662S12|training data]] consists of 550 data points (i.e. 550 points in a 13 dimensional space) along with the correct label for each point. Use this data, along with any method of your choice, to design what you think is an accurate classifier. When you are done designing your classifier, email your source code to your instructor, and you will receive the testing data.  Then '''without changing your code''', test your classifier on the testing data and note its accuracy. Summarize your method and results in a report.  
+
The [[training_data_HW3_ECE662S12|training data]] consists of 550 data points (i.e. 550 points in a 13 dimensional space) along with the correct label for each point. Use this data, along with any method of your choice, to design what you think is an accurate classifier.  
  
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If you feel like sharing your results and methods publicly, feel free to post a copy of your work below, but only '''after the deadline for the homework has past'''.  
+
==Part I==
 +
When you are done designing your classifier, write down what accuracy the classifier has, according to the training data. (This is what we call the "predicted accuracy".) Then use your classifier to classify the following [[test_data_HW3_ECE662S12|test set vectors]]. Your colleague Daniel has kindly volunteered to collect all the answers and summarize their accuracy in the table below. In order for Daniel to do that, we ask that you send him
 +
*The nickname you want him to use to identify your work in the table below;
 +
*Your predicted accuracy;
 +
*The labels you obtain for each test set vector.
 +
Hand in everything in [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=drugeles&assn=true Daniel's drop box] in a single file following this syntax:
  
−
----
 
−
The discussion page for this homework is [[hw3_discussion_ECE662_S12|here]].
 
−
----
 
−
 
−
==All versus All Classification==
 
−
 
−
If you thought this course could not been more fun, then you are wrong. With this competition, everything becomes more interesting. We are talking about a competition among the best students in the world in one of the coolest field of study, pattern recognition! Which classifier will make a better prediction for this data, SVM, Bayes, KNN .. ? How much should I fit my classifier with the training data? Hopefully we can solve this questions by the end of the competition.
 
−
 
−
For this part of the homework, all you have to do is [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=drugeles&assn=true submit here] a text file with the following schema:
 
 
<p>
 
<p>
 
Joe Blo<br />
 
Joe Blo<br />
Line 42: Line 39:
 
<br />
 
<br />
  
 +
==Part II==
 +
After the deadline for the homework has passed, I will release the ground truth labels for the test set vectors [[test_data_labels_HW3_ECE662S12|here]]. (If you are done designing your classifier before 8pm on Thursday April 26, you may obtain the ground truth labels by emailing your instructor.)  Compare the labels obtained using your classifier with the ground truth labels: the number of correctly classified vectors is the "test" accuracy. Summarize your method and results in a report. Make sure your report includes all your code, your predicted accuracy, and the confusion matrix of your classifier on the test data. Drop a pdf of your report in the HW3 assignment box in your [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=mboutin&assn=true instructor's dropbox]  before 11:59pm, Monday April 30. '''
  
−
The first row is your nickname.<br />
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==Part III (optional)==
−
The second row is your predicted results computed with the training set.<br />
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If you feel like sharing your results and methods publicly, feel free to post a summary of your method and replace your nickname by your true name in the table below. But please only do this '''after April 30'''. If you do not wish to be identified but still would like a summary of your method to appear in the table below, send it to Daniel (by email) and he will post it for you.  
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All the other rows are your prediction from the test set. Please classify keeping the order given in the test set.<br />
+
−
<br />
+
  
−
Please submit <strong>before May 2012.</strong>
 
 
----
 
----
−
==Result Summary==
+
The discussion page for this homework is [[hw3_discussion_ECE662_S12|here]].
−
This results will be computed from the submissions in drugeles dropbox. Refer to All versus All Classification section.
+
----
 +
----
 +
==Result Summary (kindly complied by your colleague Daniel)==
 +
 
 +
The following results were computed from the submissions in [https://www.projectrhea.org/rhea/index.php/Special:DropBox?forUser=drugeles&assn=true drugeles dropbox]. Refer to [[Hw3_discussion_ECE662_S12|HW3 discussion]] for details or questions.<br />
 +
 
 +
Finally our competition has come to an end. However you are more than welcome to keep experimenting on the dataset, or trying to find trends in the results from our ece662 alumni.
 +
Many of the submissions did not follow the guidelines and therefore there might be some results missing, if this is your case, please let me know, I will recompute the results and we might even find a new winner.<br />
 +
 
 +
All the data used in testing the competition and the script can be found at [http://dl.dropbox.com/u/16013127/Contestece662.zip here].<br />
 +
Only unzip and execute in the shell<br />
 +
$ python contest.py<br />
 +
 
 +
Please do not forget to share results and why not, a link to your work. We will be happy to see Purdue's talent in pattern recognition.
 +
 
 +
[[Image:shera.jpg]]
 +
 
 +
 
 
{|
 
{|
−
! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="4" |column labels go here
+
! style="background: rgb(238, 238, 238) none repeat scroll 0% 0%; -moz-background-clip: -moz-initial; -moz-background-origin: -moz-initial; -moz-background-inline-policy: -moz-initial;" colspan="4"|Results from Pattern Recognition Contest!
 
|-
 
|-
 
|Position
 
|Position
 
|Nickname (add link to method summary or report)
 
|Nickname (add link to method summary or report)
 
|Confusion Matrix
 
|Confusion Matrix
−
|Overall Accuracy
 
 
|Predicted Accuracy
 
|Predicted Accuracy
 +
|Test Set Accuracy
 +
|
 +
|Within 10%
 +
|-
 +
| 1
 +
|shera (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 45& 13& 24& 8& 0\\
 +
\mathbf{1}& 3& 6& 2& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|43.64%
 +
|50.5%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 2
 +
|ck910525 (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.73%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 3
 +
|Tim Chen (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.5455%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 4
 +
|Sherlock Holmes (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 25& 7& 0\\
 +
\mathbf{1}& 0& 0& 1& 1& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|40%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 
|-
 
|-
 
| 5
 
| 5
−
| Joe Blo (method summary)
+
|Katniss Everdeen (method summary)
 
|<math>
 
|<math>
−
\left(
+
\left( \begin{array}{cccccc}& 
−
\begin{array}{cccccc}
+
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
−
&\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
+
\mathbf{0}& 48& 19& 26& 8& 0\\
−
\mathbf{0}& x& x& x& x& x \\
+
\mathbf{1}& 0& 0& 0& 0& 0\\
−
\mathbf{1}& x& x& x& x&x \\
+
\mathbf{2}& 0& 0& 0& 0& 0\\
−
\mathbf{2}& x& x& x& x&x \\
+
\mathbf{3}& 0& 0& 0& 0& 0\\
−
\mathbf{3}& x& x& x& x& x\\
+
\mathbf{4}& 0& 0& 0& 0& 0\\
−
\mathbf{4}& x& x& x& x&x \\
+
 
\end{array}
 
\end{array}
 
\right)
 
\right)
 
</math>
 
</math>
−
|75%
+
|   42.44%
−
|77%
+
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 
|-
 
|-
 +
| 6
 +
|Joy (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 7
 +
|ECE_662_is_cool (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 37& 10& 16& 3& 0\\
 +
\mathbf{1}& 1& 5& 4& 4& 0\\
 +
\mathbf{2}& 10& 4& 6& 1& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|61.45%
 +
|47.52%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 8
 +
|Chris (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|43.4%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 9
 +
|BY SVM 1-vs-1 RBF 6 (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.7007%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 10
 +
|BY SVM 1-vs-1 Poly 2nd (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.6314%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 11
 +
|Alvin (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.55%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 12
 +
|Alpha Zeta (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 48& 19& 26& 8& 0\\
 +
\mathbf{1}& 0& 0& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.5%
 +
|47.52%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 13
 +
|parriky (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 46& 18& 26& 8& 0\\
 +
\mathbf{1}& 1& 1& 0& 0& 0\\
 +
\mathbf{2}& 1& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|46.53%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 14
 +
|chotu (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 46& 18& 25& 7& 0\\
 +
\mathbf{1}& 2& 1& 1& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 1& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|40%
 +
|46.53%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 15
 +
|Zoe (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 46& 18& 24& 6& 0\\
 +
\mathbf{1}& 2& 1& 2& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 2& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|43%
 +
|46.53%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 16
 +
|Shelan (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 43& 15& 21& 4& 0\\
 +
\mathbf{1}& 5& 4& 5& 2& 0\\
 +
\mathbf{2}& 0& 0& 0& 2& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.91%
 +
|46.53%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 17
 +
|Danny (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 46& 18& 26& 8& 0\\
 +
\mathbf{1}& 2& 1& 0& 0& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|41.73%
 +
|46.53%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 18
 +
|BY SVM 1-vs-all RBF 6 (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 43& 17& 21& 5& 0\\
 +
\mathbf{1}& 3& 2& 3& 1& 0\\
 +
\mathbf{2}& 2& 0& 2& 2& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|41.7482%
 +
|46.53%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 19
 +
|starry (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 38& 11& 18& 7& 0\\
 +
\mathbf{1}& 8& 6& 7& 1& 0\\
 +
\mathbf{2}& 2& 2& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|45.27%
 +
|44.55%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 20
 +
|Doctor Who (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 44& 19& 25& 8& 0\\
 +
\mathbf{1}& 3& 0& 0& 0& 0\\
 +
\mathbf{2}& 1& 0& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|47%
 +
|44.55%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 21
 +
|Czardas (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 38& 11& 17& 7& 0\\
 +
\mathbf{1}& 8& 6& 8& 1& 0\\
 +
\mathbf{2}& 2& 2& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|45.45%
 +
|44.55%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 22
 +
|BY SVM 1-vs-all Poly 2nd (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 42& 16& 21& 5& 0\\
 +
\mathbf{1}& 3& 0& 1& 3& 0\\
 +
\mathbf{2}& 3& 3& 3& 0& 0\\
 +
\mathbf{3}& 0& 0& 1& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|47.0474%
 +
|44.55%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 23
 +
|NaiveBoys (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 40& 15& 22& 8& 0\\
 +
\mathbf{1}& 4& 2& 2& 0& 0\\
 +
\mathbf{2}& 3& 2& 2& 0& 0\\
 +
\mathbf{3}& 1& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.55%
 +
|43.56%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 24
 +
|    ARM (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 41& 17& 21& 8& 0\\
 +
\mathbf{1}& 4& 1& 2& 0& 0\\
 +
\mathbf{2}& 2& 1& 2& 0& 0\\
 +
\mathbf{3}& 1& 0& 1& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|    42%
 +
|43.56%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 25
 +
|cheese (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 36& 14& 17& 6& 0\\
 +
\mathbf{1}& 3& 1& 3& 2& 0\\
 +
\mathbf{2}& 9& 4& 6& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|44%
 +
|42.57%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 26
 +
|Mc Awesome Man <br/>
 +
Random Forest<br />
 +
[http://dl.dropbox.com/u/16013127/mcawesome.pdf report]
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 41& 16& 25& 7& 0\\
 +
\mathbf{1}& 4& 2& 1& 1& 0\\
 +
\mathbf{2}& 2& 1& 0& 0& 0\\
 +
\mathbf{3}& 1& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|41%
 +
|42.57%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 27
 +
|Mao (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 34& 9& 20& 8& 0\\
 +
\mathbf{1}& 10& 9& 6& 0& 0\\
 +
\mathbf{2}& 4& 1& 0& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|43%
 +
|42.57%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 28
 +
|Jin (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 42& 19& 24& 8& 0\\
 +
\mathbf{1}& 5& 0& 1& 0& 0\\
 +
\mathbf{2}& 1& 0& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|63.8%
 +
|42.57%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 29
 +
|Gemini (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 34& 11& 13& 7& 0\\
 +
\mathbf{1}& 9& 6& 10& 1& 0\\
 +
\mathbf{2}& 3& 2& 3& 0& 0\\
 +
\mathbf{3}& 2& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|42.57%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 30
 +
|timetorun (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 34& 12& 13& 7& 0\\
 +
\mathbf{1}& 9& 4& 9& 1& 0\\
 +
\mathbf{2}& 5& 3& 4& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|47%
 +
|41.58%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 31
 +
|confusion matrix (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 37& 16& 18& 7& 0\\
 +
\mathbf{1}& 8& 2& 4& 1& 0\\
 +
\mathbf{2}& 2& 1& 3& 0& 0\\
 +
\mathbf{3}& 1& 0& 1& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.55%
 +
|41.58%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 32
 +
|asvyatko (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 39& 15& 21& 8& 0\\
 +
\mathbf{1}& 4& 1& 2& 0& 0\\
 +
\mathbf{2}& 3& 1& 2& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 2& 2& 1& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|44%
 +
|41.58%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 33
 +
|Ming (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 39& 15& 21& 7& 0\\
 +
\mathbf{1}& 4& 2& 4& 1& 0\\
 +
\mathbf{2}& 5& 2& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42.886%
 +
|41.58%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 34
 +
|DanRugeles<br/>
 +
Optimized KNN <br\>
 +
K=14 <br\>
 +
Distance metric=L1/2 <br\>
 +
Weighting Function= Adaptive Gaussian <br\>
 +
Feature selection by ranking (Features 8,9,10).<br\>
 +
Ranking=Bhattacharyya distance.<br\>
 +
[http://dl.dropbox.com/u/16013127/OptimizingKNN3.pdf Report]
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 35& 12& 14& 5& 0\\
 +
\mathbf{1}& 6& 2& 7& 2& 0\\
 +
\mathbf{2}& 7& 5& 5& 1& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|43.54%
 +
|41.58%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 35
 +
|MM (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 30& 8& 14& 3& 0\\
 +
\mathbf{1}& 10& 7& 8& 1& 0\\
 +
\mathbf{2}& 6& 4& 4& 4& 0\\
 +
\mathbf{3}& 2& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|45%
 +
|40.59%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 36
 +
|chimp <br/>
 +
Bayes<br/>
 +
Discriminant Functions<br/>
 +
Lazy Learners<br/>
 +
Decision Rules<br/>
 +
Decision Trees<br/>
 +
[http://dl.dropbox.com/u/16013127/ece662hw3-chimp-without-Code-version-2.pdf Report]
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 26& 10& 9& 6& 0\\
 +
\mathbf{1}& 9& 5& 8& 1& 0\\
 +
\mathbf{2}& 13& 3& 9& 1& 0\\
 +
\mathbf{3}& 0& 1& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|41%
 +
|39.6%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 37
 +
|Min (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 34& 11& 19& 8& 0\\
 +
\mathbf{1}& 7& 4& 5& 0& 0\\
 +
\mathbf{2}& 7& 4& 2& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|43.27%
 +
|39.6%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 38
 +
|M Usman Sadiq (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 23& 10& 6& 2& 0\\
 +
\mathbf{1}& 2& 1& 0& 0& 0\\
 +
\mathbf{2}& 20& 8& 16& 5& 0\\
 +
\mathbf{3}& 3& 0& 3& 0& 0\\
 +
\mathbf{4}& 0& 0& 1& 1& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|46.6667%
 +
|39.6%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 39
 +
| Marshall Zigler (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 37& 16& 19& 6& 0\\
 +
\mathbf{1}& 10& 1& 5& 2& 0\\
 +
\mathbf{2}& 1& 2& 2& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|    43.45%
 +
|39.6%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 40
 +
|WorldPeace (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 36& 16& 20& 8& 0\\
 +
\mathbf{1}& 8& 2& 5& 0& 0\\
 +
\mathbf{2}& 4& 1& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|45%
 +
|38.61%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 41
 +
|K Marshall (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 34& 14& 16& 7& 0\\
 +
\mathbf{1}& 10& 2& 6& 1& 0\\
 +
\mathbf{2}& 3& 2& 3& 0& 0\\
 +
\mathbf{3}& 1& 1& 1& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|40%
 +
|38.61%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 42
 +
|ellie (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 32& 12& 17& 4& 0\\
 +
\mathbf{1}& 14& 5& 8& 4& 0\\
 +
\mathbf{2}& 2& 2& 1& 0& 0\\
 +
\mathbf{3}& 0& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|40.16%
 +
|37.62%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 43
 +
|BY KNN (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 34& 14& 18& 7& 0\\
 +
\mathbf{1}& 9& 2& 7& 1& 0\\
 +
\mathbf{2}& 3& 3& 1& 0& 0\\
 +
\mathbf{3}& 2& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|48.54%
 +
|36.63%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 44
 +
|shawn (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 25& 10& 14& 6& 0\\
 +
\mathbf{1}& 9& 4& 7& 1& 0\\
 +
\mathbf{2}& 12& 3& 4& 1& 0\\
 +
\mathbf{3}& 2& 2& 1& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|36%
 +
|32.67%
 +
| [[Image:down.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 45
 +
|Vicky (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 26& 12& 17& 3& 0\\
 +
\mathbf{1}& 13& 4& 7& 4& 0\\
 +
\mathbf{2}& 8& 3& 2& 1& 0\\
 +
\mathbf{3}& 1& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|31.68%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 46
 +
|KH7 (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 24& 9& 9& 1& 0\\
 +
\mathbf{1}& 7& 1& 5& 1& 0\\
 +
\mathbf{2}& 0& 0& 0& 0& 0\\
 +
\mathbf{3}& 17& 9& 12& 6& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|24.59%
 +
|30.69%
 +
| [[Image:up.jpg]]
 +
|
 +
|-
 +
| 47
 +
|JRM (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 24& 15& 13& 4& 0\\
 +
\mathbf{1}& 16& 3& 9& 4& 0\\
 +
\mathbf{2}& 7& 1& 4& 0& 0\\
 +
\mathbf{3}& 1& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|66%
 +
|30.69%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 48
 +
|Dom (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 26& 11& 18& 6& 0\\
 +
\mathbf{1}& 12& 3& 6& 1& 0\\
 +
\mathbf{2}& 7& 5& 2& 1& 0\\
 +
\mathbf{3}& 3& 0& 0& 0& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|30%
 +
|30.69%
 +
| [[Image:up.jpg]]
 +
| [[Image:target.jpg]]
 +
|-
 +
| 49
 +
|neergil (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 20& 7& 12& 4& 0\\
 +
\mathbf{1}& 14& 4& 10& 3& 0\\
 +
\mathbf{2}& 9& 5& 4& 0& 0\\
 +
\mathbf{3}& 5& 3& 0& 1& 0\\
 +
\mathbf{4}& 0& 0& 0& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|47%
 +
|28.71%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 50
 +
|Luck (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 21& 9& 13& 3& 0\\
 +
\mathbf{1}& 17& 3& 8& 3& 0\\
 +
\mathbf{2}& 9& 4& 3& 2& 0\\
 +
\mathbf{3}& 1& 2& 1& 0& 0\\
 +
\mathbf{4}& 0& 1& 1& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|40%
 +
|26.73%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 51
 +
|Anshu K=3 (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 21& 9& 13& 3& 0\\
 +
\mathbf{1}& 17& 3& 8& 3& 0\\
 +
\mathbf{2}& 9& 4& 3& 2& 0\\
 +
\mathbf{3}& 1& 2& 1& 0& 0\\
 +
\mathbf{4}& 0& 1& 1& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|26.73%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 52
 +
|Anshu K=1 (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 21& 9& 13& 3& 0\\
 +
\mathbf{1}& 17& 3& 8& 3& 0\\
 +
\mathbf{2}& 9& 4& 3& 2& 0\\
 +
\mathbf{3}& 1& 2& 1& 0& 0\\
 +
\mathbf{4}& 0& 1& 1& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|38%
 +
|26.73%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 53
 +
|OCEAN'S FOURTEEN (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 15& 8& 11& 6& 0\\
 +
\mathbf{1}& 15& 4& 10& 1& 0\\
 +
\mathbf{2}& 7& 2& 3& 0& 0\\
 +
\mathbf{3}& 6& 2& 1& 1& 0\\
 +
\mathbf{4}& 5& 3& 1& 0& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|22.77%
 +
| [[Image:down.jpg]]
 +
|
 +
|-
 +
| 54
 +
|MS (method summary)
 +
|<math>
 +
\left( \begin{array}{cccccc}& 
 +
\mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\
 +
\mathbf{0}& 2& 0& 0& 0& 0\\
 +
\mathbf{1}& 21& 10& 12& 6& 0\\
 +
\mathbf{2}& 1& 0& 3& 0& 0\\
 +
\mathbf{3}& 21& 9& 9& 1& 0\\
 +
\mathbf{4}& 3& 0& 2& 1& 0\\
 +
\end{array}
 +
\right)
 +
</math>
 +
|42%
 +
|15.84%
 +
| [[Image:down.jpg]]
 +
|
 +
 +
 +
|-
 
|}
 
|}
 +
 +
 +
 +
 +
Petition:<br/>
 +
Can you give some extra statistics on the results of the class e.g, frequency distribution of three-types of students as follows:<br />
 +
Type-1: how many students accuracy increased from what they predicted?<br />
 +
Type-2: how many students accuracy decreased from what they predicted?<br />
 +
Type-3: how many students accuracy remained within 5% of what they predicted?<br />
 +
 +
Seeing this result would actually give an insight on the percentage of people who successfully designed a good classifier.<br/>
 +
 +
Thanks,<br/>
 +
chimp<br/>
 +
 +
Response:<br/>
 +
I added to columns to satisfy your petition and here are the answers to your question:
 +
25 Classifiers where within 10% of their estimation.<br />
 +
23 Classifiers where overstimated.<br />
 +
31 Classifiers where understimated.<br />
 +
 +
 +
Petition:<br />
 +
<Please post your own petitions here, or send an email to drugeles@purdue.edu><br />
 +
 +
Comments:<br />
 +
<Please post your coments here, or send an email to drugeles@purdue.edu><br />
 
----
 
----
 
[[2012_Spring_ECE_662_Boutin|Back to ECE 662 Spring 2012]]
 
[[2012_Spring_ECE_662_Boutin|Back to ECE 662 Spring 2012]]

Latest revision as of 06:19, 25 June 2012


Third Homework, ECE662 Spring 2012

  • Drop test set output, predicted accuracy, and proposed nickname into Daniel's drop box before 11:59pm, Friday April 27, 2012.
  • Report due before 11:59pm, Monday April 30, in your instructor's Rhea dropbox. Make sure to drop it in the correct homework folder!!!!. It is the somewhere in the middle of the page.

Automatic Pattern Recognition Contest!

An anonymous company has agreed to share real data with us, so we are going to have a little contest using this data! The data comes from a five-class classification problem using 13 features. We are looking for the student who will design the most accurate classifier using this data.

The training data consists of 550 data points (i.e. 550 points in a 13 dimensional space) along with the correct label for each point. Use this data, along with any method of your choice, to design what you think is an accurate classifier.

Part I

When you are done designing your classifier, write down what accuracy the classifier has, according to the training data. (This is what we call the "predicted accuracy".) Then use your classifier to classify the following test set vectors. Your colleague Daniel has kindly volunteered to collect all the answers and summarize their accuracy in the table below. In order for Daniel to do that, we ask that you send him

  • The nickname you want him to use to identify your work in the table below;
  • Your predicted accuracy;
  • The labels you obtain for each test set vector.

Hand in everything in Daniel's drop box in a single file following this syntax:

Joe Blo
77%
1
0
1
2
3
4
2
3
1
2
0
2
1

Part II

After the deadline for the homework has passed, I will release the ground truth labels for the test set vectors here. (If you are done designing your classifier before 8pm on Thursday April 26, you may obtain the ground truth labels by emailing your instructor.) Compare the labels obtained using your classifier with the ground truth labels: the number of correctly classified vectors is the "test" accuracy. Summarize your method and results in a report. Make sure your report includes all your code, your predicted accuracy, and the confusion matrix of your classifier on the test data. Drop a pdf of your report in the HW3 assignment box in your instructor's dropbox before 11:59pm, Monday April 30.

Part III (optional)

If you feel like sharing your results and methods publicly, feel free to post a summary of your method and replace your nickname by your true name in the table below. But please only do this after April 30. If you do not wish to be identified but still would like a summary of your method to appear in the table below, send it to Daniel (by email) and he will post it for you.

The discussion page for this homework is here.

Result Summary (kindly complied by your colleague Daniel)

The following results were computed from the submissions in drugeles dropbox. Refer to HW3 discussion for details or questions.

Finally our competition has come to an end. However you are more than welcome to keep experimenting on the dataset, or trying to find trends in the results from our ece662 alumni. Many of the submissions did not follow the guidelines and therefore there might be some results missing, if this is your case, please let me know, I will recompute the results and we might even find a new winner.

All the data used in testing the competition and the script can be found at here.
Only unzip and execute in the shell
$ python contest.py

Please do not forget to share results and why not, a link to your work. We will be happy to see Purdue's talent in pattern recognition.

Shera.jpg


Results from Pattern Recognition Contest!
Position Nickname (add link to method summary or report) Confusion Matrix Predicted Accuracy Test Set Accuracy Within 10%
1 shera (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 45& 13& 24& 8& 0\\ \mathbf{1}& 3& 6& 2& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43.64% 50.5% Up.jpg
2 ck910525 (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.73% 47.52% Up.jpg
3 Tim Chen (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.5455% 47.52% Up.jpg
4 Sherlock Holmes (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 25& 7& 0\\ \mathbf{1}& 0& 0& 1& 1& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 40% 47.52% Up.jpg
5 Katniss Everdeen (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.44% 47.52% Up.jpg
6 Joy (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42% 47.52% Up.jpg
7 ECE_662_is_cool (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 37& 10& 16& 3& 0\\ \mathbf{1}& 1& 5& 4& 4& 0\\ \mathbf{2}& 10& 4& 6& 1& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 61.45% 47.52% Down.jpg
8 Chris (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43.4% 47.52% Up.jpg Target.jpg
9 BY SVM 1-vs-1 RBF 6 (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.7007% 47.52% Up.jpg
10 BY SVM 1-vs-1 Poly 2nd (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.6314% 47.52% Up.jpg
11 Alvin (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.55% 47.52% Up.jpg
12 Alpha Zeta (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 48& 19& 26& 8& 0\\ \mathbf{1}& 0& 0& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.5% 47.52% Up.jpg
13 parriky (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 46& 18& 26& 8& 0\\ \mathbf{1}& 1& 1& 0& 0& 0\\ \mathbf{2}& 1& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42% 46.53% Up.jpg
14 chotu (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 46& 18& 25& 7& 0\\ \mathbf{1}& 2& 1& 1& 0& 0\\ \mathbf{2}& 0& 0& 0& 1& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 40% 46.53% Up.jpg
15 Zoe (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 46& 18& 24& 6& 0\\ \mathbf{1}& 2& 1& 2& 0& 0\\ \mathbf{2}& 0& 0& 0& 2& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43% 46.53% Up.jpg Target.jpg
16 Shelan (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 43& 15& 21& 4& 0\\ \mathbf{1}& 5& 4& 5& 2& 0\\ \mathbf{2}& 0& 0& 0& 2& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.91% 46.53% Up.jpg Target.jpg
17 Danny (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 46& 18& 26& 8& 0\\ \mathbf{1}& 2& 1& 0& 0& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 41.73% 46.53% Up.jpg
18 BY SVM 1-vs-all RBF 6 (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 43& 17& 21& 5& 0\\ \mathbf{1}& 3& 2& 3& 1& 0\\ \mathbf{2}& 2& 0& 2& 2& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 41.7482% 46.53% Up.jpg
19 starry (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 38& 11& 18& 7& 0\\ \mathbf{1}& 8& 6& 7& 1& 0\\ \mathbf{2}& 2& 2& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 45.27% 44.55% Down.jpg Target.jpg
20 Doctor Who (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 44& 19& 25& 8& 0\\ \mathbf{1}& 3& 0& 0& 0& 0\\ \mathbf{2}& 1& 0& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 47% 44.55% Down.jpg Target.jpg
21 Czardas (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 38& 11& 17& 7& 0\\ \mathbf{1}& 8& 6& 8& 1& 0\\ \mathbf{2}& 2& 2& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 45.45% 44.55% Down.jpg Target.jpg
22 BY SVM 1-vs-all Poly 2nd (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 42& 16& 21& 5& 0\\ \mathbf{1}& 3& 0& 1& 3& 0\\ \mathbf{2}& 3& 3& 3& 0& 0\\ \mathbf{3}& 0& 0& 1& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 47.0474% 44.55% Down.jpg Target.jpg
23 NaiveBoys (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 40& 15& 22& 8& 0\\ \mathbf{1}& 4& 2& 2& 0& 0\\ \mathbf{2}& 3& 2& 2& 0& 0\\ \mathbf{3}& 1& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.55% 43.56% Up.jpg Target.jpg
24 ARM (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 41& 17& 21& 8& 0\\ \mathbf{1}& 4& 1& 2& 0& 0\\ \mathbf{2}& 2& 1& 2& 0& 0\\ \mathbf{3}& 1& 0& 1& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42% 43.56% Up.jpg Target.jpg
25 cheese (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 36& 14& 17& 6& 0\\ \mathbf{1}& 3& 1& 3& 2& 0\\ \mathbf{2}& 9& 4& 6& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 44% 42.57% Down.jpg Target.jpg
26 Mc Awesome Man

Random Forest
report

$ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 41& 16& 25& 7& 0\\ \mathbf{1}& 4& 2& 1& 1& 0\\ \mathbf{2}& 2& 1& 0& 0& 0\\ \mathbf{3}& 1& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 41% 42.57% Up.jpg Target.jpg
27 Mao (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 34& 9& 20& 8& 0\\ \mathbf{1}& 10& 9& 6& 0& 0\\ \mathbf{2}& 4& 1& 0& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43% 42.57% Down.jpg Target.jpg
28 Jin (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 42& 19& 24& 8& 0\\ \mathbf{1}& 5& 0& 1& 0& 0\\ \mathbf{2}& 1& 0& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 63.8% 42.57% Down.jpg
29 Gemini (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 34& 11& 13& 7& 0\\ \mathbf{1}& 9& 6& 10& 1& 0\\ \mathbf{2}& 3& 2& 3& 0& 0\\ \mathbf{3}& 2& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42% 42.57% Up.jpg Target.jpg
30 timetorun (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 34& 12& 13& 7& 0\\ \mathbf{1}& 9& 4& 9& 1& 0\\ \mathbf{2}& 5& 3& 4& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 47% 41.58% Down.jpg
31 confusion matrix (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 37& 16& 18& 7& 0\\ \mathbf{1}& 8& 2& 4& 1& 0\\ \mathbf{2}& 2& 1& 3& 0& 0\\ \mathbf{3}& 1& 0& 1& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.55% 41.58% Down.jpg Target.jpg
32 asvyatko (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 39& 15& 21& 8& 0\\ \mathbf{1}& 4& 1& 2& 0& 0\\ \mathbf{2}& 3& 1& 2& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 2& 2& 1& 0& 0\\ \end{array} \right) $ 44% 41.58% Down.jpg Target.jpg
33 Ming (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 39& 15& 21& 7& 0\\ \mathbf{1}& 4& 2& 4& 1& 0\\ \mathbf{2}& 5& 2& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42.886% 41.58% Down.jpg Target.jpg
34 DanRugeles

Optimized KNN <br\> K=14 <br\> Distance metric=L1/2 <br\> Weighting Function= Adaptive Gaussian <br\> Feature selection by ranking (Features 8,9,10).<br\> Ranking=Bhattacharyya distance.<br\> Report

$ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 35& 12& 14& 5& 0\\ \mathbf{1}& 6& 2& 7& 2& 0\\ \mathbf{2}& 7& 5& 5& 1& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43.54% 41.58% Down.jpg Target.jpg
35 MM (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 30& 8& 14& 3& 0\\ \mathbf{1}& 10& 7& 8& 1& 0\\ \mathbf{2}& 6& 4& 4& 4& 0\\ \mathbf{3}& 2& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 45% 40.59% Down.jpg Target.jpg
36 chimp

Bayes
Discriminant Functions
Lazy Learners
Decision Rules
Decision Trees
Report

$ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 26& 10& 9& 6& 0\\ \mathbf{1}& 9& 5& 8& 1& 0\\ \mathbf{2}& 13& 3& 9& 1& 0\\ \mathbf{3}& 0& 1& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 41% 39.6% Down.jpg Target.jpg
37 Min (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 34& 11& 19& 8& 0\\ \mathbf{1}& 7& 4& 5& 0& 0\\ \mathbf{2}& 7& 4& 2& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43.27% 39.6% Down.jpg Target.jpg
38 M Usman Sadiq (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 23& 10& 6& 2& 0\\ \mathbf{1}& 2& 1& 0& 0& 0\\ \mathbf{2}& 20& 8& 16& 5& 0\\ \mathbf{3}& 3& 0& 3& 0& 0\\ \mathbf{4}& 0& 0& 1& 1& 0\\ \end{array} \right) $ 46.6667% 39.6% Down.jpg
39 Marshall Zigler (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 37& 16& 19& 6& 0\\ \mathbf{1}& 10& 1& 5& 2& 0\\ \mathbf{2}& 1& 2& 2& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 43.45% 39.6% Down.jpg Target.jpg
40 WorldPeace (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 36& 16& 20& 8& 0\\ \mathbf{1}& 8& 2& 5& 0& 0\\ \mathbf{2}& 4& 1& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 45% 38.61% Down.jpg
41 K Marshall (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 34& 14& 16& 7& 0\\ \mathbf{1}& 10& 2& 6& 1& 0\\ \mathbf{2}& 3& 2& 3& 0& 0\\ \mathbf{3}& 1& 1& 1& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 40% 38.61% Down.jpg Target.jpg
42 ellie (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 32& 12& 17& 4& 0\\ \mathbf{1}& 14& 5& 8& 4& 0\\ \mathbf{2}& 2& 2& 1& 0& 0\\ \mathbf{3}& 0& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 40.16% 37.62% Down.jpg Target.jpg
43 BY KNN (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 34& 14& 18& 7& 0\\ \mathbf{1}& 9& 2& 7& 1& 0\\ \mathbf{2}& 3& 3& 1& 0& 0\\ \mathbf{3}& 2& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 48.54% 36.63% Down.jpg
44 shawn (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 25& 10& 14& 6& 0\\ \mathbf{1}& 9& 4& 7& 1& 0\\ \mathbf{2}& 12& 3& 4& 1& 0\\ \mathbf{3}& 2& 2& 1& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 36% 32.67% Down.jpg Target.jpg
45 Vicky (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 26& 12& 17& 3& 0\\ \mathbf{1}& 13& 4& 7& 4& 0\\ \mathbf{2}& 8& 3& 2& 1& 0\\ \mathbf{3}& 1& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 42% 31.68% Down.jpg
46 KH7 (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 24& 9& 9& 1& 0\\ \mathbf{1}& 7& 1& 5& 1& 0\\ \mathbf{2}& 0& 0& 0& 0& 0\\ \mathbf{3}& 17& 9& 12& 6& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 24.59% 30.69% Up.jpg
47 JRM (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 24& 15& 13& 4& 0\\ \mathbf{1}& 16& 3& 9& 4& 0\\ \mathbf{2}& 7& 1& 4& 0& 0\\ \mathbf{3}& 1& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 66% 30.69% Down.jpg
48 Dom (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 26& 11& 18& 6& 0\\ \mathbf{1}& 12& 3& 6& 1& 0\\ \mathbf{2}& 7& 5& 2& 1& 0\\ \mathbf{3}& 3& 0& 0& 0& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 30% 30.69% Up.jpg Target.jpg
49 neergil (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 20& 7& 12& 4& 0\\ \mathbf{1}& 14& 4& 10& 3& 0\\ \mathbf{2}& 9& 5& 4& 0& 0\\ \mathbf{3}& 5& 3& 0& 1& 0\\ \mathbf{4}& 0& 0& 0& 0& 0\\ \end{array} \right) $ 47% 28.71% Down.jpg
50 Luck (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 21& 9& 13& 3& 0\\ \mathbf{1}& 17& 3& 8& 3& 0\\ \mathbf{2}& 9& 4& 3& 2& 0\\ \mathbf{3}& 1& 2& 1& 0& 0\\ \mathbf{4}& 0& 1& 1& 0& 0\\ \end{array} \right) $ 40% 26.73% Down.jpg
51 Anshu K=3 (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 21& 9& 13& 3& 0\\ \mathbf{1}& 17& 3& 8& 3& 0\\ \mathbf{2}& 9& 4& 3& 2& 0\\ \mathbf{3}& 1& 2& 1& 0& 0\\ \mathbf{4}& 0& 1& 1& 0& 0\\ \end{array} \right) $ 42% 26.73% Down.jpg
52 Anshu K=1 (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 21& 9& 13& 3& 0\\ \mathbf{1}& 17& 3& 8& 3& 0\\ \mathbf{2}& 9& 4& 3& 2& 0\\ \mathbf{3}& 1& 2& 1& 0& 0\\ \mathbf{4}& 0& 1& 1& 0& 0\\ \end{array} \right) $ 38% 26.73% Down.jpg
53 OCEAN'S FOURTEEN (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 15& 8& 11& 6& 0\\ \mathbf{1}& 15& 4& 10& 1& 0\\ \mathbf{2}& 7& 2& 3& 0& 0\\ \mathbf{3}& 6& 2& 1& 1& 0\\ \mathbf{4}& 5& 3& 1& 0& 0\\ \end{array} \right) $ 42% 22.77% Down.jpg
54 MS (method summary) $ \left( \begin{array}{cccccc}& \mathbf{0}&\mathbf{1}&\mathbf{2}&\mathbf{3}&\mathbf{4}\\ \mathbf{0}& 2& 0& 0& 0& 0\\ \mathbf{1}& 21& 10& 12& 6& 0\\ \mathbf{2}& 1& 0& 3& 0& 0\\ \mathbf{3}& 21& 9& 9& 1& 0\\ \mathbf{4}& 3& 0& 2& 1& 0\\ \end{array} \right) $ 42% 15.84% Down.jpg




Petition:
Can you give some extra statistics on the results of the class e.g, frequency distribution of three-types of students as follows:
Type-1: how many students accuracy increased from what they predicted?
Type-2: how many students accuracy decreased from what they predicted?
Type-3: how many students accuracy remained within 5% of what they predicted?

Seeing this result would actually give an insight on the percentage of people who successfully designed a good classifier.

Thanks,
chimp

Response:
I added to columns to satisfy your petition and here are the answers to your question: 25 Classifiers where within 10% of their estimation.
23 Classifiers where overstimated.
31 Classifiers where understimated.


Petition:
<Please post your own petitions here, or send an email to drugeles@purdue.edu>

Comments:
<Please post your coments here, or send an email to drugeles@purdue.edu>

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Basic linear algebra uncovers and clarifies very important geometry and algebra.

Dr. Paul Garrett