Marine ML Benchmark: A Comprehensive Benchmark for Machine Learning in Marine Science

A comprehensive benchmark dataset and codebase for evaluating machine learning models in marine science applications. This package includes: - 9 marine datasets (159,851 total samples) covering biotoxin detection, oceanographic measurements, satellite data, and phytoplankton analysis- 37 pre-trained models across 5 algorithms (Random Forest, XGBoost, SVR, LSTM, Transformer)- Complete reproducible pipeline with cross-platform scripts- Publication-ready figures and tables- Comprehensive documentation and methodology The benchmark evaluates both traditional machine learning and deep learning approaches on diverse marine science tasks, providing standardized evaluation metrics and reproducible results for the research community. Key Features:- Cross-sectional and time-series marine datasets- Traditional ML vs Deep Learning comparison- Comprehensive data validation and sanity checks- Publication-ready visualizations- Complete reproducibility package- Multi-platform support (Windows/Linux/Mac) This work supports reproducible research in marine machine learning and provides a foundation for future developments in the field.