What type of models can you build using Databricks?

Databricks provides a comprehensive platform that supports a variety of machine learning paradigms, making it versatile for different types of model development. The inclusion of supervised, unsupervised, and reinforcement learning models indicates that users can build predictive models based on labeled data (supervised), discover patterns in unlabeled data (unsupervised), and create agents that learn from interacting with an environment (reinforcement learning).

Supervised learning allows for tasks like classification and regression, where we train models on labeled datasets. Unsupervised learning enables tasks such as clustering and dimensionality reduction without pre-existing labels. Reinforcement learning focuses on training models to make sequences of decisions by maximizing cumulative rewards through trial and error.

The other options are too limiting, only covering specific types of models or methods within machine learning, which does not reflect the full capabilities of the Databricks platform in its current state. By supporting all three categories, Databricks empowers data scientists and engineers to explore a wide range of applications and achieve various data-driven objectives.