Which of the following algorithms is designed for multi-label classification?

The Amazon SageMaker image classification algorithm is specifically designed to handle multi-label classification tasks. In multi-label classification, each instance can be associated with multiple labels simultaneously, which is common in image datasets where an image might depict multiple objects or concepts at once.

The image classification algorithm uses neural networks and convolutional layers that allow it to learn complex patterns in the data and recognize multiple features within a single image. This capability makes it suitable for applications like tagging images with multiple relevant tags or recognizing different objects within a single picture, generating outputs that reflect the presence of different labels.

In contrast, the other algorithms listed are not geared towards multi-label classification. For instance, XGBoost is typically used for binary or multi-class classification tasks rather than multi-label scenarios. The K-Means algorithm is primarily used for clustering, which is focused on grouping data points into distinct clusters rather than classifying them with multiple labels. Lastly, the time series forecasting algorithm is designed specifically for predicting future values based on past trends and does not inherently handle multi-label classification tasks either. Thus, the image classification algorithm stands out as the right choice for multi-label scenarios.