The training process involves feeding the labeled data into the model, which then makes predictions. These predictions are compared to the actual labels, and the differences are used to adjust the model's parameters. This process is repeated until the model's predictions are sufficiently accurate. Techniques like cross-validation are often used to ensure the model generalizes well to new, unseen data.
