So, in the case of a binary logistic regression model, the dependent variable is a logit of p, with p being the probability that the dependent variables have a value of 1.

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 ...

Additionally, each binary logistic regression procedure will have a probability of an incorrect result and combining multiple procedures will result in a high probability of an incorrect result. Multi ...

In addition to predicting the value of a variable (e.g., a patient will survive), logistic regression can also predict the associated probability (e.g., the patient has a 75% chance of survival).

The computed pseudo-probability output is 0.0765 and because that value is less than 0.5 the prediction is class 0 = male. This article assumes you have an intermediate or better familiarity with a ...

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 ...