Predicting soil textural classes using random forest models: learning from imbalanced dataset

HIGHLIGHTS

• who: Sina Mallah and colleagues from the data resamplingdata resamplingExtension Organization (AREEO), Karaj, Iran Department of Geosciences, Soil Science and Geomorphology, University of Tu00fcbingen have published the Article: Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset, in the Journal: Agronomy 2022, 2613 of 29/08/2021
• what: The aim of this study was to investigate the effects of imbalance in training data on the performance of a random forest model (RF). The authors should consider the main purpose of resampling techniques is not improving the accuracy of models but enhancing the . . .
  

   

  

  If you want to have access to all the content you need to log in!

  Thanks :)

  Username or Email

  Password

  Remember me

  Lost your password?

  If you don't have an account, you can create one here.