Method Drift›Parameter-efficient fine-tuning (LoRA family)
Random Forest
Parameter-efficient fine-tuning (LoRA family)
superseded — cited as a baseline and beaten by newer methods
1 papers critique it · 1 beat it on benchmarks
What papers say
Verbatim critique sentences, each from a paper that cites Random Forest as a baseline.
“limited by parameter accuracy and generalization, they struggle with flexible predictions in real-world scenarios.”
— StellarF: A Lora-Adapter Integrated Large Model Framework for Stellar Flare Forecasting with Historical & Statistical Data
Beaten on benchmarks
Head-to-head results where a newer method reports beating Random Forest. Values are copied from the source paper's tables — verify against the cited paper.
- iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
iLoRA beats Random Forest · F1 (UC) [IBD diagnosis (tabular)]
0.6557 vs 0.5753
- iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
iLoRA beats Random Forest · AUROC [IBD diagnosis (tabular)]
0.7990 vs 0.6151
- iLoRA: Bayesian Low-Rank Adaptation with Latent Interaction Graphs for Microbiome Diagnosis
iLoRA beats Random Forest · AUPRC [IBD diagnosis (tabular)]
0.7617 vs 0.6467
Newer alternatives
Recent methods in the same sub-problem, not yet superseded in the knowledge base.