An introspective comparison of random forest-based classifiers for the analysis of cluster-correlated data by way of RF++

Mathematics

• Error Rate 100%
• Replicate 66%
• Independent Sample 33%
• Training Data 33%
• Median 33%
• Postprocessing 33%
• Command Line 33%
• Processing Step 33%
• Clustered Data 33%

Computer Science

• Correlated Data 100%
• Random Decision Forest 100%
• Feature Selection 33%
• Postprocessing Step 16%
• Command-Line 16%
• Graphical User Interface 16%
• Classification Error 16%
• Training Data 16%
• Classification Algorithm 16%
• Common Practice 16%
• Independent Sample 16%
• Clustered Data 16%

Earth and Planetary Sciences

• Bootstrapping 100%
• Mass Spectroscopy 33%
• Graphical User Interface 33%