Core Concepts

Abstract. This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. First we ... Logistic regression ... logistic_reg() defines a generalized linear model for binary outcomes. A linear combination of the predictors is used to model the log ... Mar 5, 2013 ... I now believe that McFadden's R 2 is a better choice. However, I've also learned about another R 2 that has good properties, a lot of intuitive appeal, and is ... Mar 1, 2021 ... The change of direction in OR within the depression variable only occurs with the specific addition of the two anxiety variables. May 20, 2016 ... Title:Coresets for Scalable Bayesian Logistic Regression ... Abstract:The use of Bayesian methods in large-scale data settings is attractive ... May 1, 2013 ... Mixed effects logistic regression models have become widely used statistical models to model clustered binary responses. We prove that this optimization problem admits a tractable reformulation and encapsulates the classical as well as the popular regularized logistic regression. In natural language processing, logistic regression is the base- line supervised machine learning algorithm for classification, and also has a very close ... LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control. Yingtian Hu, Glen A. Boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum Bernoulli likelihood as a criterion.