User:Abby.Martin
From REU@MU
Revision as of 15:21, 12 June 2015 by Abby.Martin (Talk | contribs)
Contents
Project
Mentor: Dr. Richard Povinelli
I will be researching the application and accuracy of linear regression model trees. I aim to test the effectiveness of this method in assisting with electric load forecasting. I also plan on comparing this method of forecasting to a multitude of other methods that have also been attempted.
Goals & Milestones
- Test the influence of linear regression model trees on the accuracy of electrical use forecasting.
- Determine if linear regression model trees are a better method of forecasting electric load forecasting than other methods.
- Create a linear regression model tree using MATLAB for use in the electric portion of GasDay/ apply data and methods found/created to data from the GasDay lab.
- Research linear regression model trees and electrical usage.
- Continue research on linear regression model trees, electrical usage, and MATLAB. Start creating methods for forecasting electrical use using linear regression model trees.
- Continue research on linear regression model trees and electrical usage. Also learn how to effectively use MATLAB.
- Test my linear regression model tree with real data and compare its effectiveness with that of other forecasting methods.
Weekly Goals
Week One
- Read and research papers that address the following topics:
- Decision Trees
- Machine Learning
- Model Trees
- Linear Regression Model Trees
- Electric Load Forecasting and other methods that have been used
Week Two
- Continue reading about linear regression model trees
- Begin testing various datasets using the WEKA software
- Begin reading some of the source code and documentation to better understand WEKA
- Begin learning, using, and applying MATLAB
Week Three
- Test real data using the WEKA software to create various model trees.
- Determine what settings are best for forecasting data.
Weekly Log
Week One
- Orientation activities and forms
- Pre-REU Survey
- Attended GasDay Camp
- Met Dr.Povinelli and decided on research topic
- Read papers on my topic to discover:
- the definition and application of decision trees
- difference between classification trees, regression trees, and model trees
- machine learning and how trees split
Week Two
- Met with Dr. Povinelli to further discuss concepts and goals
- Explained:
- the "greedy" approach
- the various ways to determine the "best" variable and tree
- suggested reading about the M5P Model
- Explained:
- Read about the M5P Model and learned:
- Splits using a Standard Deviation Reduction Method
- Uses a smoothing method for leaves
- Article contained helpful pseudocode for understanding the process of creating a linear regression model tree
- Began working with the WEKA software to create linear regression model trees
- Read some of the source code from WEKA to understand the linear regression model tree creation process
- Began working with and learning MATLAB