In statistics, logistic regression or logit regression is a type of probabilistic classification model[1] used for predicting the outcome of a categorical dependent variable (i.e., a class label) based on e or more predictor variables (features). That is, it is used in estimating empirical values of the parameters in a qualitative response model. The probabilities describing the possible outcomes of a single trial are modeled, as a function of the explanatory (predictor) variables, using a logistic function. Frequently (and subsequently in this article) "logistic regression" is used to refer specifically to the problem in which the dependent variable is binary—that is, the number of available categories is two—and problems with more than two categories are referred to as multinomial logistic regression or, if the multiple categories are ordered, as ordered logistic regression.

logisitic_exercise\LogisticRegressionCost.m
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logisitic_exercise

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