Logistic regression is a classification algorithm. It is used to predict a binary outcome (0/1, Yes/No, True/False) from the set of independent variables. It is a special case of linear regression where the outcome variable is categorical. The log of odds is the dependent variables, that is, it predicts the probability of occurrence of an event by fitting data to a logit function. Logistic regression is also termed as linear classification model. The link function used in the logistic regression is the logic link 1/(1+exp(-wTx)). The related loss function for logistic regression is the logistic loss, that is, log(1+exp(-ywTx)). Here y is the actual target variable (either 1 for the positive class or -1 for the negative class).
This recipe shows how to apply the logistic regression algorithm available in the Spark MLlib package on Bank Marketing Data. The code is written in Scala.
