![]() ![]() 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. ![]()
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