In a binary logistic regression model, the dependent variable has two levels ( categorical). Analogous models with a different sigmoid function instead of the logistic function can also be used, such as the probit model the defining characteristic of the logistic model is that increasing one of the independent variables multiplicatively scales the odds of the given outcome at a constant rate, with each independent variable having its own parameter for a binary dependent variable this generalizes the odds ratio.

The unit of measurement for the log-odds scale is called a logit, from logistic un it, hence the alternative names. The corresponding probability of the value labeled '1' can vary between 0 (certainly the value '0') and 1 (certainly the value '1'), hence the labeling the function that converts log-odds to probability is the logistic function, hence the name. In the logistic model, the log-odds (the logarithm of the odds) for the value labeled '1' is a linear combination of one or more independent variables ('predictors') the independent variables can each be a binary variable (two classes, coded by an indicator variable) or a continuous variable (any real value). Mathematically, a binary logistic model has a dependent variable with two possible values, such as pass/fail which is represented by an indicator variable, where the two values are labeled '0' and '1'. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic model (a form of binary regression). Logistic regression is a statistical model that in its basic form uses a logistic function to model a binary dependent variable, although many more complex extensions exist.

Each object being detected in the image would be assigned a probability between 0 and 1, with a sum of one.

This can be extended to model several classes of events such as determining whether an image contains a cat, dog, lion, etc. In statistics, the logistic model (or logit model) is used to model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. It is not to be confused with Logit function.

Gx works 2 parameter