classification algorithms logistic regression introduction to logistic regression logistic regression is supervised learning classification algorithm used to predict the probability of target variable the nature of target or dependent variable is dichotomouswhich means there would be only two possible classes in simple wordsthe dependent variable is binary in nature having data coded as either (stands for success/yesor (stands for failure/nomathematicallya logistic regression model predicts ( = as function of it is one of the simplest ml algorithms that can be used for various classification problems such as spam detectiondiabetes predictioncancer detection etc types of logistic regression generallylogistic regression means binary logistic regression having binary target variablesbut there can be two more categories of target variables that can be predicted by it based on those number of categorieslogistic regression can be divided into following typesbinary or binomial in such kind of classificationa dependent variable will have only two possible types either and for examplethese variables may represent success or failureyes or nowin or loss etc multinomial in such kind of classificationdependent variable can have or more possible unordered types or the types having no quantitative significance for examplethese variables may represent "type aor "type bor "type cordinal in such kind of classificationdependent variable can have or more possible ordered types or the types having quantitative significance for examplethese variables may represent "pooror "good""very good""excellentand each category can have the scores like , , , logistic regression assumptions before diving into the implementation of logistic regressionwe must be aware of the following assumptions about the same