Logistic regression is a statistical model used to analyse and predict the relationship between a binary dependent variable (only two possible values) and one or more independent variables, which can be categorical or continuous. It is a type of regression analysis used in machine learning and data mining.
Logistic regression is based on the logistic or sigmoidal function, which is an S-shaped curve that models the probability of the dependent variable having a given value as a function of the independent variables. The logistic function converts any input value into a value between 0 and 1, which is interpreted as the probability of the event occurring.
The goal of logistic regression is to find the coefficients that best fit the data and most accurately predict the probability that the dependent variable will take one of two possible values. The coefficients are fitted by an iterative optimisation process that minimises the error in predicting the values of the dependent variable.
The world is experiencing exponential growth in data generation on an ever-increasing scale. According to IDC (International Data Corp.
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