Categorical scale data are only ordinal and defined on a finite set. Continuous bounded interval. Due to that character, the statistical methods for ought be based orders between outcomes not any metric involving distance measure. For simple two-sample data, variants of classical rank suitable. regression type problems, there known good generalized linear models separate categories long time. I...

Learning a categorical distribution comes with its own set of challenges. A successful approach taken by state-of-the-art works is to cast the problem in continuous domain take advantage impressive performance generative models for data. Amongst them are recently emerging diffusion probabilistic models, which have observed generating high-quality samples. Recent advances focused on log likeliho...

Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression such scenarios, including variable screening, model selection, order selection response categories, and selection. perform our gene expression with 801 patients, 2426 genes, five ...

Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package valuable since there currently large range of Machine Learning (ML) algorithms that can only be trained using numerical (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with attributes. Currently, CANE of...