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.
Artificial intelligence is increasingly present in companies and its growth is being applied in practically all sectors. When the end [...]
Read More »In the previous articles ("Basic concepts to build a commercial software with artificial intelligence" and "How to materialize the opportun [...]
Read More »Artificial intelligence (AI) can change the way sales channels and customers are managed for manufacturers and distributors of consumer products, and can [...]
Read More »It is convenient that by means of a brief questionnaire we are able to verify the viability of a business opportunity. Next, develop [...]
Read More »
