The data doctor continues his exploration of Python-based machine learning techniques, explaining binary classification using logistic regression, which he likes for its simplicity. The goal of a ...

The fundamental technique has been studied for decades, thus creating a huge amount of information and alternate variations that make it hard to tell what is key vs. non-essential information.

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort ...

We show how conditional logistic regression can be used to estimate the probability of being enumerated in a census and apply the model to the 1990 Post-Enumeration Survey (PES) in the United States.

Results from classic linear regression regarding the effect of adjusting for covariates upon the precision of an estimator of exposure effect are often assumed to apply more generally to other types ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

What are the advantages of logistic regression over decision trees? originally appeared on Quora: the place to gain and share knowledge, empowering people to learn from others and better understand ...