Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

By Yingzhi Lin, Xiangzheng Deng, Xing Li, Enjun Ma. Frontiers in Earth Science, 2014

57 citations recorded by the source corpus.

Subfields: Environmental Chemistry, Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/c24236259378ec90b9cebb3940d79c532c8c4f3c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. B. E. Owusu
2. P. Tongkumchum
3. Ralf Wieland
4. Claas Nendel
5. Suhaimee Buya
6. Tobia Lakes

Citations in the corpus

• Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China (2021, methodology): “Most such models are statistical models, such as logistic regression models or models based on principle component analysis (Li et al., 2013; Lin et al., 2014) or Bayesian belief networks (Krüger and Lakes, 2015) Some such models are spatially explicit (e. .”
• Modelling of land-use change in Thailand using binary logistic regression and multinomial logistic regression (2020, background): “There are already some studies that compared logistic regression and multinomial logistic regression to predict land- use change such as Lin et al. (2014) in a case study area of the Jiangxi Province, China.”