What are the research directions in improving the accuracy and interpretability of logistic regression? What are the potential pitfalls of ensembling?

... logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict ... Jan 4, 2017 ... First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend ... Abstract. This paper addresses the important tradeoff between privacy and learnability, when designing algorithms for learning from private databases. First we ... A supervised learning algorithm that can be used to classify data into categories, or classes, by predicting the probability that an observation falls into a ... Logistic regression converts the relative probability of any subgroup into a logarithmic number, called a regression coefficient, that can be added or ... Mar 17, 2023 ... To create the model, declare a logistic_reg() model. This needs mixture and penalty arguments which control the amount of regularization. A ... Feb 16, 2001 ... First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend ... Jun 20, 2012 ... The c-statistic follows a standard normal cumulative distribution function with dependence on the product of the standard deviation of the normal components. Jul 26, 2002 ... A procedure by Firth originally developed to reduce the bias of maximum likelihood estimates is shown to provide an ideal solution to separation. Logistic regression definition: Logistic regression is a statistical model, also called the logit model, designed to find a relationship between two data ...