https://github.com/anellenson/DecisionTree_WaveForecasts

This is the live demo of the bagged regression tree paper.
https://github.com/anellenson/DecisionTree_WaveForecasts

Last synced: about 2 months ago
JSON representation

This is the live demo of the bagged regression tree paper.

• Host: GitHub
• URL: https://github.com/anellenson/DecisionTree_WaveForecasts
• Owner: anellenson
• Created: 2019-07-12T21:56:33.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-04-15T17:59:01.000Z (over 4 years ago)
• Last Synced: 2024-08-03T18:15:15.389Z (5 months ago)
• Language: Jupyter Notebook
• Size: 2.9 MB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-coastal - anellenson/DecisionTree_WaveForecasts

README

        
# An application of a machine learning algorithm to determine and describe error patterns within wave model output

This is the live demo to accompany the manuscript: 

Ellenson, A., Pei, Y., Wilson, G., Özkan-Haller, H. T., & Fern, X. (2020). An application of a machine learning algorithm to determine and describe error patterns within wave model output. Coastal Engineering, 157, 103595.. 

In this repo, you'll find a jupyter notebook that trains and 'explores' the decision tree, corresponding code and wave model data. The jupyter notebook is comprised of two parts. The first part shows how to train and test a bagged regression tree. The second shows how to explore the tree architecture to understand how the decision tree partitions the data and finds regions of wave model error.