In this new study where data collection was completed in May of 2023, Know Labs applied novel data preprocessing techniques and trained a Light Gradient-Boosting Machine (lightGBM) model to ...

Researchers developed a machine learning model using CatBoost to predict disturbances in drone formations with an R² of 83.3%, significantly improving from the previous baseline of 54%.

Machine learning (ML) is about making predictions about new data based on old data. The quality of any machine-learning algorithm is ultimately determined by the quality of those predictions. However, ...

Researchers have devised a new machine learning method to improve large-scale climate model projections and demonstrated that the new tool makes the models more accurate at both the global and ...

But, despite the importance of model accuracy, it is possible to focus on it too much in real-world R&D. A ‘near-perfect’ model – typically considered a model that predicts outputs reliably to within ...

Microsoft researchers believe they have found a way to use machine learning to push those limits for small molecules (arXiv 2025, DOI: 10.48550/arXiv.2506.14665).

10 ways machine learning projects fail AI hallucinations Model bias Legal and ethical risks Poor data quality Model overfitting and underfitting Legacy system integration issues Performance and ...

Empirical observation of negligible fairness–accuracy trade-offs in machine learning for public policy. Nature Machine Intelligence, 2021; 3 (10): 896 DOI: 10.1038/s42256-021-00396-x ...

All in all, not all advanced machine learning models are black box, and for most applications, a degree of explainability is sufficient to meet legal and regulatory requirements.

The good news is that there are many on the market—and they are generally affordable. But regardless of what path you take, there needs to be a clear-cut business case for machine learning.