Gradient boosting is primarily used for which type of modeling?

Gradient boosting is a powerful machine learning technique that is versatile in nature and is commonly used for both prediction and classification tasks. Its fundamental approach combines the predictions of multiple weak learners, typically decision trees, to create a strong predictive model.

In the context of prediction, gradient boosting algorithms can be used to forecast continuous outcomes, making them suitable for regression problems. For classification tasks, gradient boosting helps in categorizing data points into discrete classes. By adjusting parameters and using appropriate loss functions, the model can be tailored to effectively handle either type of task.

This adaptability is why the correct answer encompasses both prediction and classification, highlighting gradient boosting's role in addressing a wide range of modeling scenarios within the realm of data science.