Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

MIT researchers have developed a method that generates more accurate uncertainty measures for certain types of estimation.

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...

Felimban, R. (2025) Financial Prediction Models in Banks: Combining Statistical Approaches and Machine Learning Algorithms.

As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

This special report introduces small area estimation as a modern approach for producing reliable, stand-level forest ...

Machine learning models delivered the strongest performance across nearly all evaluation metrics. CHAID and CART provided the highest and most stable sensitivity, accuracy and discriminatory power, ...