Which Amazon SageMaker algorithm is designed for solving classification or regression problems?

The Linear Learner algorithm in Amazon SageMaker is specifically designed for handling both classification and regression problems. It employs linear regression for regression tasks and performs logistic regression for binary classification tasks, effectively modeling relationships between the input features and the target variable using a linear approach. This algorithm can provide interpretable models, which is a significant advantage in many applications where understanding the influence of features is important.

Linear Learner is versatile and can efficiently handle large datasets. It uses optimization techniques such as mini-batch stochastic gradient descent which allows it to scale effectively with the size of the data. By selecting hyperparameters like regularization techniques, one can prevent overfitting and enhance model performance on new, unseen data.

Other algorithms like Decision Trees, Random Forest, and Boosted Trees, while also suitable for classification and regression tasks, do not carry the same linear assumptions and interpretations that the Linear Learner does. They use different methodologies, such as ensemble learning or aggregating multiple trees, which can lead to different properties in terms of interpretation and performance depending on dataset characteristics.