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Logistic regression is a supervised learning algorithm used in machine learning to predict the probability of a binary outcome. A binary outcome is limited to one of two possible outcomes. Examples include yes/no, 0/1 and true/false.
Logical regression is used in predictive modeling to analyze large datasets in which one or more independent variables can determine an outcome. The outcome is expressed as a dichotomous variable that has one of two possible outcomes.
Essentially, logistic regression works by estimating the mathematical probability that an instance belongs to a specified class — or not.
Techopedia Explains Logistic Regression
Logistic regression uses something called the Sigmoid function to map predicted predictions and their probabilities. On a graph, if the estimated probability is greater than a pre-defined acceptance threshold, then the model will predict that the instance belongs to that class. If the estimated probability is less than the pre-defined threshold on the graph, then the model will predict the instance does not belong to the class.
In statistics, there are three basic types of logistic regression:
Binary logistic regression — useful for predicting the relationship between a binary dependent variable (Y) and an independent variable (X).
Multinomial logistic regression — useful for making predictions when the dependent variable has two or more discrete outcomes and the order of the outcomes doesn’t matter.
Ordinal logistic regression — useful for making predictions when the dependent variable has more than two discrete outcomes and the order of the outcomes has some significance.
