The Overfitting Challenge in Blockchain Analysis

Machine learning models tend to overfit when used with blockchain datasets causing a phenomenon known as overfitting. Overfitting occurs when a model generates a hypothesis that is too tailored to a specific dataset to the data making it impossible to adapt to new datasets. The obvious answer to fight overfitting is to use larger training datasets but that’s not always an option. At IntoTheBlock, we regularly encounter overfitting challenges and we rely on a series of basic recipes to address it. There are three simple, almost common sense, rules that help prevent this phenomenon in deep learning applications.