When analyzing repeated measures designs—such as a patient’s evolution or a machine’s logs—an error in modeling the individual can lead to misleading conclusions, like false positives. To avoid this, ASCA+ correctly models individual variability, ensuring that results are robust, reliable, and reproducible, even with complex and unbalanced data.
Modeling the individual in Repeated Measures Models with ASCA+ Read Post »
In an increasingly digitalized industrial world, model interpretability is key. At Datharsis, we transform complex data into clear, reliable decisions, empowering industrial experts. Discover how our 100% interpretable models help you predict quality, monitor processes, and comply with regulations, such as the EU AI Act.
Interpretability driving industry Read Post »
In today’s cybersecurity, analyst shortages and sophisticated threats are a challenge. Data science emerges as key to automating and transparenting analysis, allowing analysts to focus on critical tasks and transform cybersecurity.
Revolutionizing cybersecurity Read Post »
Which variables should you actually interpret in a statistical model?
In this article, we explain how Sparse PCA (SPCA) — and a new corrected version — can help you identify key variables, such as biomarkers, without losing interpretability. A practical approach from biostatistics for projects with lots of data and little certainty.
Sparse PCA and biomarkers: understanding more, assuming less Read Post »
Discover how Artificial Intelligence is transforming business decision-making processes. This Datharsis post explores key AI applications, the importance of data governance, and offers practical advice for companies to adopt AI strategically and securely.
AI in business transformation Read Post »
