Huber loss is primarily designed for regression tasks, where the goal is to predict a continuous target variable. However, it can be adapted for classification tasks by using a modified version called the Huberized hinge loss. This loss function combines the properties of the hinge loss (used in Support Vector Machines) and the Huber loss, making it more robust to outliers and noise in classification problems.