Logistic regression was used to develop a risk prediction model using the FIT result and screening data: age, sex and previous screening history.

Some results are presented on improving the fit of the logistic regression model for binary data by transforming the vector of explanatory variables. The methods are based on consideration of the ...

The log-logistic distribution has a non-monotonic hazard function which makes it suitable for modelling some sets of cancer survival data. A log-logistic regression model is described in which the ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Regression is a method to estimate parameters in mathematical models of biological systems from experimental data. To ensure the validity of a model for a given data set, pre-regression and post ...

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

Because the logistic regression model was trained using normalized and encoded data, the x-input must be normalized and encoded in the same way. Notice the double square brackets on the x-input.

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