Despite its name, Logistic Regression is a classification algorithm, using a linear model (i.e., it computes the {{ isCausalPrediction() | targetRoleName}} feature as a linear combination of input features).

Logistic Regression minimizes a specific cost function (called logit or sigmoid function), which makes it appropriate for classification.
A simple Logistic regression algorithm is prone to overfitting and sensitive to errors in the input dataset. To address these issues, it is possible to use a penalty (or regularization term) to the weights.
This implementation can use either 'L1' or 'L2' regularization terms.