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

What are the advantages of logistic regression over decision trees? This question was originally answered on Quora by Claudia Perlich.

This article will cover the basic theory behind logistic regression, the types of logistic regression, when to use them and take you through a worked example.

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

Logistic regression enables you to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome, or response, can be dichotomous (yes, no) or ordinal (low ...

In logistics regression, you can use machine learning to help predict the probability of the outcome of a situation with two potentials. For instance, it is good for predicting whether something ...

Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are ...

Binary-response regression models in which the link function is a family defined by one or more unknown shape parameters are considered. Detailed attention is given to the two single-parameter ...

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems.