Random Forest

Knowledge graph centered on Random Forest with 72 nodes and 155 connections. Top connected: leaf water content, precipitation, Colorado River, elevation gradient, thermal constraints.

Method synopsis

Random Forest regression to predict ecosystem drought sensitivity from hydrological and topographic variables. Uses bootstrapped subsampling with multiple regression trees to identify key predictive variables and their importance rankings.

Synthesized from method descriptions across 4 papers using this protocol.

Procedure from a recent peer-reviewed implementation

Steps below were extracted from the most recent peer-reviewed implementation of this protocol in the corpus — Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions (2024), Frontiers in Water. Implementations in other papers (listed below) may differ.

1. Random Forest regression analysis
   Used Random Forest method to connect hydrological features with drought sensitivity. RF generates many regression trees from bootstrapped subsampled data and averages over all trees. Number of trees set to 800, randomly sampling 10% of hillslopes as training set at each iteration and averaging coefficients over multiple iterations.
   Quantities: 800 regression trees, 10% training set selection, candidate variables = total variables divided by 3, minimum terminal node size = 5Duration: Multiple iterations with random samplingConditions: Applied to statistical relationships between drought sensitivity and hydrological variables
   Equipment: R software, randomForest package

Typical Equipment

• R software
• randomForest package

Output Measurements

• drought sensitivity predictions
• variable importance rankings
• model performance metrics

Papers Using This Protocol (4)

Species Studied With This Protocol

Places Where Used