What is the initial step to create a custom trainable classifier?

The initial step to create a custom trainable classifier involves seeding with sample data. This is crucial because the classifier needs high-quality, representative examples of the data it will analyze in order to learn and improve its predictive capabilities. By providing sample data, you set the foundation for the training process, allowing the model to learn patterns and relationships within the data.

This step is essential since the classifier's performance heavily relies on the quality and relevance of the training data used. Without adequate sample data, the classifier cannot accurately identify or classify additional documents. Once the model has been trained using this seed data, it can then proceed to testing, publishing, or applying sensitivity labels as later steps in the process.