What type of learning model would you typically use for building a predictive model with labeled data?

Supervised learning is the correct choice for building a predictive model with labeled data because it specifically involves training a model on a dataset that includes both input features and corresponding target labels. In supervised learning, the model learns to map inputs to outputs by identifying patterns in the labeled data, allowing it to make predictions on new, unseen data.

This approach is commonly employed in various applications such as classification, where the goal is to categorize data points, and regression, where the aim is to predict continuous values. The presence of labeled data is crucial, as it serves as the foundation for guiding the model's learning process, enabling it to evaluate its performance based on how accurately it predicts the labels of the training data.

In contrast, reinforcement learning focuses on training models through feedback from their actions in an environment, rather than relying on labeled datasets. Unsupervised learning, on the other hand, works with data that has no labeled responses, aiming to find hidden patterns or groupings without predefined outputs. Transfer learning involves taking a pre-trained model and adapting it to a different but related task, which also relies on supervised learning principles rather than working directly with the data from the start.