Spring 2020 Project 3: Assess Learners - Revision history

Jfox44: /* Revisions */

2020-02-08T01:02:23Z

Revisions

2020-02-08T01:01:17Z

Implement author() (Up to 10 point penalty)

2020-02-08T00:15:34Z

Revisions

2020-02-08T00:15:08Z

Implement author() (Up to 10 point penalty)

2020-01-31T15:10:58Z

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2020-01-31T15:10:29Z

Revisions

2020-01-31T15:10:06Z

Revisions

2020-01-31T15:09:55Z

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2020-01-31T15:09:09Z

Required, Allowed & Prohibited

2020-01-30T20:01:38Z

Overview

← Older revision		Revision as of 01:02, 8 February 2020
Line 8:		Line 8:

	* 1/31/2020 Updated Required, Allowed & Prohibited section to clarify chart generation.		* 1/31/2020 Updated Required, Allowed & Prohibited section to clarify chart generation.
−	* 2/7/2020 Revised example author() method

	==Overview==		==Overview==

@@ Line 169: / Line 169: @@
      def author(self):
-         return '123456789' # replace 123456789 with your Georgia Tech 9 digit student number.
+         return 'tb34' # replace tb34 with your Georgia Tech user id.
 </pre>

← Older revision		Revision as of 00:15, 8 February 2020
Line 8:		Line 8:

	* 1/31/2020 Updated Required, Allowed & Prohibited section to clarify chart generation.		* 1/31/2020 Updated Required, Allowed & Prohibited section to clarify chart generation.
		+	* 2/7/2020 Revised example author() method

	==Overview==		==Overview==

@@ Line 168: / Line 168: @@
      def author(self):
-         return 'tb34' # replace tb34 with your Georgia Tech username.
+         return '123456789' # replace 123456789 with your Georgia Tech 9 digit student number.
 </pre>

@@ Line 7: / Line 7: @@
 This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.
-* 1/31/2020 Updated Required, Allowed, and Prohibited to clarify chart generation.
+* 1/31/2020 Updated Required, Allowed & Prohibited section to clarify chart generation.
 ==Overview==

← Older revision		Revision as of 15:09, 31 January 2020
Line 6:		Line 6:

	This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.		This assignment is subject to change up until 3 weeks prior to the due date. We do not anticipate changes; any changes will be logged in this section.
		+
		+	1/31/2020 Updated Required, Allowed, and Prohibited to clarify chart generation.

	==Overview==		==Overview==

@@ Line 280: / Line 280: @@
 * Your code must run in less than 10 seconds per test case 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. *Note that all charts you provide must be generated in Python (in testlearner.py)
+* The learner code files (DT, RT, etc.) you submit should NOT generate any output: No prints, no charts, etc. testlearner.py is the only file that should generate charts. *Note that all charts you provide must be generated in Python
 Allowed:

@@ Line 9: / Line 9: @@
 ==Overview==
-You are to implement and evaluate three learning algorithms as Python classes: A "classic" Decision Tree learner, a Random Tree learner, and a Bootstrap Aggregating learner.  Note that a Linear Regression learner is provided for you in the git repository.  The classes should be named DTLearner, RTLearner,  and BagLearner respectively.  The Linear Regression learner is already named LinRegLearner.
+You are to implement and evaluate three learning algorithms as Python classes: A "classic" Decision Tree learner, a Random Tree learner, and a Bootstrap Aggregating learner.  Note that a Linear Regression learner is provided for you in the assess learners zip file.  The classes should be named DTLearner, RTLearner,  and BagLearner respectively.  The Linear Regression learner is already named LinRegLearner.
 Note that we are considering this a <b>regression</b> problem (not classification).  So the goal for your learner is to return a continuous numerical result (not a discrete result).  In this project we are ignoring the time order aspect of the data and treating it as if it is static data and time does not matter. In a later project we will make the transition to considering time series data.