An evaluation framework from the paper Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?

This repository provides a unified framework for evaluating modern deep neural networks on small tabular datasets, evaluated on 31 field- and farm-scale digital soil mapping datasets from LimeSoDa.

Overview

• Datasets: Uses soil datasets from the LimeSoDa repository with proximal soil sensing and remote sensing features.

• Models: Implements 15+ models with a unified interface:

  • Classical ML: Linear Regression, Ridge, Lasso, PLSR, Random Forest, XGBoost
  • MLP-based NNs: MLP, TabM, RealMLP
  • Retrieval-based NNs: TabR, ModernNCA
  • Attention-based NNs: AutoInt, FT-Transformer, ExcelFormer, T2G-Former, AMFormer
  • In-context learning foundation models: TabPFN

• Configuration: Experiment settings defined via YAML configuration files. Configuration files for datasets with feature-to-sample ratio < 1 are in the config/pss/ folder, while configurations for high-dimensional datasets with ratio > 1 (including MIR/NIR spectroscopy features) are in the config/spectroscopic/ folder.

• Preprocessing: Built-in support for PCA, feature scaling, numerical embeddings

Setup

Requirements: Python 3.10+

pip install -r requirements.txt

Usage

Run experiments using YAML configuration files:

python benchmark.py --config config/pss/limesoda_mlp.yaml

Example configuration files are provided in config/pss/ and config/spectroscopic/ folders.

Results & Data

Complete experimental results, including optimized hyperparameters for all dataset-model combinations and model predictions, are available: results.tar.gz

Citation

@misc{barkov2025modern,
  title         = {Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale {Digital} {Soil} {Mapping}?},
  author        = {Viacheslav Barkov and Jonas Schmidinger and Robin Gebbers and Martin Atzmueller},
  year          = {2025},
  eprint        = {2508.09888},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2508.09888},
}