logistic regression

Aug 30 2023

Firth’s Logit: Some References

Carlisle Rainey

In Rainey and McCaskey (2021), Kelly McCaskey and I offer a accessible and practical (re)introduction to Firth’s penalized maximum likelihood estimator that (1) corrects the small sample bias and (2) reduces the excessive variance of the usual maximum likelihood estimator. Below, I bookmark other...

logistic regression firth methodology small samples

Aug 11 2023

Benchmarking Firth’s Logit: {brglm2} versus {logistf}

Carlisle Rainey

Firth’s Logit I like Firth’s logistic regression model (Firth 1993). I talk about that in Rainey and McCaskey (2021) and this Twitter thread. Kosmidis and Firth (2021) offer an excellent, recent follow-up as well. I’ll refer you to the papers for a careful discussion of the benefits, but Firth’s...

computing logistic regression r firth small samples

Feb 23 2020

A simple and interpretable performance measure for a binary classifier

msuzen

Kindly reposted to KDnuggets by Gregory Piatetsky-Shapiro  Preamble The core application of machine learning models is a binary classification task. This appears in polyhedra of areas from medicine for diagnostic tests to credit risk decision making for consumers.  Techniques in building...

performance machine learning data science data analysis deep learning statistical learning supervised learning roc ba logistic regression statistics binary classification balanced accuracy pcc auc