What is the term for a data set that has been cleaned and labeled for training a machine learning model?

The term for a data set that has been cleaned and labeled specifically for training a machine learning model is "training data." This dataset serves as the foundation for the learning process, enabling the model to learn patterns, features, and relationships in the data that it will later apply to make predictions on new, unseen data.

In the context of machine learning, training data is crucial because it directly influences the model's ability to generalize and perform well on real-world tasks. During the training phase, the algorithm analyzes the training data, adjusting its internal parameters to minimize error in its predictions or classifications based on the examples provided.

Other data types, such as testing data, validation data, and synthetic data, serve different purposes in the machine learning pipeline. Testing data is used to evaluate the model's performance after training, validation data assists in tuning and optimizing the model's hyperparameters, and synthetic data involves artificially generated samples that can sometimes be used to augment or augment the original dataset.