Through trend analyses, this surveillance highlighted both the emergence and decline of AMR across diverse bacterial pathogens, helping inform which antibiotics may remain appropriate as first-line ...

Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental ...

Regression explains how changes in one factor influence another with clarity. Each regression type is suited for different data patterns and problems. Regression remains fast, reliable, and widely ...

The longitudinal microbiome compositional data are highly skewed, bounded in [0,1), and often sparse with many zeros. In addition, the observations from repeated measures are correlated. We propose a ...

Machine learning and deep learning have been widely embraced, and even more widely misunderstood. In this article, I’ll step back and explain both machine learning and deep learning in basic terms, ...

High-throughput sequencing of 16S gene or metagenomes provides an unprecedented opportunity to discover microbes associated with traits such as clinical outcomes or environmental factors. However, the ...

Abstract: We consider the binary classification problem of static and dynamic mixed data in this paper. Different from mixed categorical and numerical data, the dynamic variables in the new type of ...

Abstract: Everal real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. Credit scoring is a typical example ...

Dr. James McCaffrey of Microsoft Research demonstrates applying the L-BFGS optimization algorithm to the ML logistic regression technique for binary classification -- predicting one of two possible ...

Some machine learning models belong to either the “generative” or “discriminative” model categories. Yet what is the difference between these two categories of models? What does it mean for a model to ...