An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-Sensed Data

Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any...

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Vydáno v:Remote sensing (Basel, Switzerland) Ročník 8; číslo 11; s. 943
Hlavní autoři: Gu, Yingxin, Wylie, Bruce, Boyte, Stephen, Picotte, Joshua, Howard, Daniel, Smith, Kelcy, Nelson, Kurtis
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2016
Témata:
Accuracy
Case studies
Climate change
Cubist optimization
data mining
Datasets
Ecosystem models
Ecosystems
Environment models
Geology
Landsat
Mathematical models
MODIS NDVI
Optimization
overfitting
Python scripts
Regression analysis
regression tree mapping model
Remote sensing
Software
Strategy
Training
Trends
underfitting
United States > US
ISSN:2072-4292, 2072-4292
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Shrnutí:Regression tree models have been widely used for remote sensing-based ecosystem mapping. Improper use of the sample data (model training and testing data) may cause overfitting and underfitting effects in the model. The goal of this study is to develop an optimal sampling data usage strategy for any dataset and identify an appropriate number of rules in the regression tree model that will improve its accuracy and robustness. Landsat 8 data and Moderate-Resolution Imaging Spectroradiometer-scaled Normalized Difference Vegetation Index (NDVI) were used to develop regression tree models. A Python procedure was designed to generate random replications of model parameter options across a range of model development data sizes and rule number constraints. The mean absolute difference (MAD) between the predicted and actual NDVI (scaled NDVI, value from 0–200) and its variability across the different randomized replications were calculated to assess the accuracy and stability of the models. In our case study, a six-rule regression tree model developed from 80% of the sample data had the lowest MAD (MADtraining = 2.5 and MADtesting = 2.4), which was suggested as the optimal model. This study demonstrates how the training data and rule number selections impact model accuracy and provides important guidance for future remote-sensing-based ecosystem modeling.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs8110943