Training a machine learning model involves which of the following?

Training a machine learning model fundamentally involves using historical data to learn patterns. This process is essential because it enables the model to identify relationships and features within the data that can be generalized to make predictions on new, unseen data. During training, the model adjusts its parameters based on the input features and corresponding output labels to minimize the error in its predictions. This learning process is at the heart of model development and sets the foundation for the subsequent stages, such as evaluation, deployment, and logging experiments.

While evaluating model performance, deploying the model, and logging experiments are all critical activities within the broader machine learning lifecycle, they occur after or as part of the broader training process, rather than being the core focus of training itself.