In the context of blockchain analysis, the bias-variance friction is present everywhere. Let's go back to our algorithm that attempts to predict price with a number of blockchain factors. If we use a simple linear regression method, the model is likely to underfit. However, if we use a super complex neural network with a small dataset, the model is likely to overfit. Using machine learning to analyze blockchain data is a very nascent space. As a result, most of the models are encountering the traditional challenges with machine learning applications. Overfitting is one of those omnipresent challenges in blockchain analysis fundamentally due to the lack of labeled data and trained models. There is no magic solution to fight overfitting but some of the principles outlined in this article have proven to be effective for us at IntoTheBlock.

Using machine learning to analyze blockchain datasets is a fascinating challenge. Beyond the incredible potential of uncovering unknown insights that help us understand the behavior of crypto-assets, blockchain datasets presents very unique challenges to a machine learning practitioner. Many of these challenges translate into major roadblocks for most traditional machine learning techniques. However, the rapid evolution of machine intelligence technologies has enabled the creation of novel machine learning methods that result very applicable to the analysis of blockchain datasets. At IntoTheBlock, we regularly experiment with these new methods to improve the efficiency of our market intelligence signals.