Assess learners - Revision history

Tucker: /* Implement author() Method (up to 10 point penalty) */

2018-09-27T20:02:52Z

Implement author() Method (up to 10 point penalty)

2018-09-27T20:02:29Z

Implement author() Method (up to 10 point penalty)

2018-09-27T20:01:58Z

Implement author() Method (up to 10 point penalty)

2018-09-17T16:42:26Z

Required, Allowed & Prohibited

2018-09-17T16:35:13Z

Updates

2018-09-15T22:34:57Z

2018-09-14T15:54:45Z

Updates=

2018-09-14T15:45:57Z

Still in Draft

2018-09-14T15:32:19Z

Rubric

2018-09-14T15:30:31Z

Still in Draft

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 ==Implement author() Method (up to 10 point penalty)==
-For all learners you submit (DT, RT, Bag, Insane) should implement a method called <tt>author()</tt> that returns your Georgia Tech user ID as a string.  This must be implemented within <b>'each individual file.</B>  It is not your 9 digit student number.  Here is an example of how you might implement author() within a learner object:
+For all learners you submit (DT, RT, Bag, Insane) should implement a method called <tt>author()</tt> that returns your Georgia Tech user ID as a string.  This must be implemented within <b>each individual file.</B>  It is not your 9 digit student number.  Here is an example of how you might implement author() within a learner object:
 <pre>

@@ Line 155: / Line 155: @@
 ==Implement author() Method (up to 10 point penalty)==
-For all learners you submit (DT, RT, Bag, Insane) should implement a method called <tt>author()</tt> that returns your Georgia Tech user ID as a string.  This must be implemented within '''each individual file.'''  It is not your 9 digit student number.  Here is an example of how you might implement author() within a learner object:
+For all learners you submit (DT, RT, Bag, Insane) should implement a method called <tt>author()</tt> that returns your Georgia Tech user ID as a string.  This must be implemented within <b>'each individual file.</B>  It is not your 9 digit student number.  Here is an example of how you might implement author() within a learner object:
 <pre>

@@ Line 155: / Line 155: @@
 ==Implement author() Method (up to 10 point penalty)==
-For all learners you submit (DT, RT, Bag, Insane) should implement a method called <tt>author()</tt> that returns your Georgia Tech user ID as a string.  It is not your 9 digit student number.  Here is an example of how you might implement author() within a learner object:
+For all learners you submit (DT, RT, Bag, Insane) should implement a method called <tt>author()</tt> that returns your Georgia Tech user ID as a string.  This must be implemented within '''each individual file.'''  It is not your 9 digit student number.  Here is an example of how you might implement author() within a learner object:
 <pre>

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 * Your project must be coded in Python 2.7.x.
 * Your code must run on one of the university-provided computers (e.g. buffet02.cc.gatech.edu).
-* Your code must run in less than 5 seconds on one of the university-provided computers.
+* Your code must run in less than 10 seconds on one of the university-provided computers.
 * The code you submit should NOT include any data reading routines.  The provided testlearner.py code reads data for you.
 * The code you submit should NOT generate any output: No prints, no charts, etc.

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 * Each "bag" in bag learner must use a different subset of the data.
 * When quantitatively comparing two learning methods, you cannot use correlation as a metric.
 ==Overview==

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 * Quantitatively compare "classic" decision trees (DTLearner) versus random trees (RTLearner).  In which ways is one method better than the other?  Provide at least two quantitative measures. Note that for this part of the report you must conduct new experiments, don't use the results of the experiments above for this.
-Note that all charts you provide must be generated in Python, and you must submit the python code you used to generate the plots.
+Note that all charts you provide must be generated in Python (in <tt>testlearner.py</tt>), and you must submit the python code you used to generate the plots.
 ==Hints & resources==
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 Via Canvas, submit as individual files.
-* Your code as <tt>RTLearner.py</tt>, <TT>DTLearner.py</TT>, <tt>InsaneLearner.py</tt> and <tt>BagLearner.py</tt>.
+* Your code as <tt>RTLearner.py</tt>, <TT>DTLearner.py</TT>, <tt>InsaneLearner.py</tt>, <tt>BagLearner.py</tt>, and <tt>testlearner.py</tt>.
 * Your report as <tt>report.pdf</tt>

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 * Q: How many data items should be in each bag? A: If the training set is of size N, each bag should contain N items.  Note that since sampling is with replacement some of the data items will be repeated.
-==Updates===
+==Updates==
 '''2018-09-14'''

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-==Still in Draft==
+==FAQ==
 * Q: Can I use an ML library or do I have to write the code myself?  A: You must write the decision tree and bagging code yourself.  The LinRegLearner is provided to you. Do not use other libraries or your code will fail the auto grading test cases.
@@ Line 10: / Line 10: @@
 * Q: How many data items should be in each bag? A: If the training set is of size N, each bag should contain N items.  Note that since sampling is with replacement some of the data items will be repeated.
 '''2018-09-14'''

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 * Comparison of DT and RT learning
 ** Is each quantitative experiment explained well enough that someone else could reproduce it (up to -5 points if not)
-** Are there at least two new quantitative properties that are compared? (-5 points if only one, -10 if none)
+** Are there at least two new quantitative properties that are compared (not correlation)? (-5 points if only one, -10 if none)
 ** Is each conclusion regarding each comparison supported well with charts? (up to -10 points if not)