What is the role of training data in machine learning?

Training data serves the essential purpose of teaching a machine learning model to recognize patterns and make predictions based on new, unseen data. By providing a diverse and representative set of examples, training data allows the model to learn the underlying relationships between input features and corresponding output values. This learning process is fundamental for enabling the model to generalize its understanding and accurately predict outcomes when presented with similar data later on.

The quality and quantity of training data directly influence the model's ability to perform well. Without sufficient training data, the model may struggle to identify the necessary patterns, leading to inadequate predictions and potentially reinforcing bias if the data is not representative. Thus, training data is crucial in developing a model that not only predicts outcomes but does so with a high degree of accuracy.