Reproducibility in machine learning
Lack of reproducibility in your machine learning projects could be a waste of resources and decrease the credibility of your models and findings in your research projects. Reproducibility is not the only term used in this context; there are also two other key terms: repeatability and replicability. We don’t want to get into the details of these differences. Instead, we want to have a definition of reproducibility to use in this book. We define reproducibility in machine learning as the ability of different individuals or teams of scientists and developers to achieve the same results using the same dataset, methodology, and development environment as reported in an original report or study. We can ensure reproducibility through the proper sharing of code, data, model parameters and hyperparameters, and other relevant information, which allows others to validate and build upon our findings. Let’s better understand the importance of reproducibility...
