Multi-method feature importance analysis

Knowledge graph centered on Multi-method feature importance analysis with 12 nodes and 22 connections. Top connected: Phytoplankton, riparian vegetation, Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Environmental Molecular Sciences Laboratory, Molecular diversity of dissolved organic matter re.

Method synopsis

Systematic feature selection using six complementary statistical methods to identify key environmental drivers of chemodiversity while reducing bias from any single technique.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — Molecular diversity of dissolved organic matter reflects macroecological patterns in river networks (2025), Scientific Reports. Implementations in other papers (listed below) may differ.

1. Feature importance analysis
   Applied multiple feature importance methods (n=6) to bolster the selection process reliability including Pearson correlation coefficient, Spearman's rank correlation coefficient, F-test, Mutual information (MI), Random Forest (RF), and SHAPley values. A correlation coefficient threshold was set at an absolute value of > 0.23. Variables achieving an average importance score above 0.5 were identified as significantly influential.
   Quantities: 6 statistical methods, threshold > 0.23 for correlation, importance score > 0.5Duration: Not specifiedConditions: Computational analysis
   Equipment: Statistical software including R, Python non-linear least squares

Typical Equipment

• Statistical software including R
• Python non-linear least squares

Output Measurements

• feature importance scores
• environmental driver rankings

Papers Using This Protocol (1)

Species Studied With This Protocol

Places Where Used