H2O Reproducibility Guidelines for Machine Learning

This document is a guide that outlines the principles of reproducibility in H2O, emphasizing its significance in scientific research, data analysis, and machine learning experiments. It details various factors that can influence reproducibility, including randomness in data splitting, model training, and the design of machine learning methods. The guide provides a series of guidelines to ensure that analyses yield consistent results across different executions. Key recommendations include controlling data splitting by setting a random-number seed, ensuring hyperparameters remain constant, and being cautious with early stopping during model training. Additionally, it advises on managing parallelism during cluster initialization to avoid variations in outcomes due to different operational orders. The document also stresses the importance of using the same version of H2O to maintain consistency in results. Overall, these guidelines aim to assist users in achieving reliable and reproducible outcomes in their machine learning tasks using H2O.