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.
Software as a Service (SaaS) companies have gained enormous prominence in the last few years, mainly due to the novelty of the products [...]
Read More »The semantic web or "internet of knowledge" is an extension of the current web. Unlike the latter, the semantic web is based on proportional [...]
Read More »As e-commerce continues to grow at a dizzying pace, fraudsters are also finding new and sophisticated ways to exploit the potential [...]
Read More »Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
Read More »
