Logistic Regression, Stochastic Gradient, Regularization.
and maximum is 1. So, for instance sigmoid(1) = 0.73, sigmoid(0.1) = 0.524, sigmoid(10) = 0.999…., sigmoid(500) = 0.999…, sigmoid(-500) = 0.000….. Thus, extreme outliers on either side are mitigated. This is why we use the sigmoid function in Logistic Regression. Main Benefits of sigmoid Sigmoid