In the context of AI, what does the term 'model training' refer to?

Model training is a fundamental concept in AI that pertains to the practice of feeding data into a model to enhance its ability to make accurate predictions or decisions. During the training process, the model learns patterns and relationships within the data through algorithms, adjusting its parameters to minimize errors in predictions. This iterative process allows the model to adapt and improve over time as it encounters varying datasets.

By utilizing a large and diverse set of training data, the model can generalize its understanding to new, unseen examples, which is crucial for its effectiveness in real-world applications. This approach allows the model to learn complex functions and make informed predictions based on input data while continuously refining its abilities based on feedback and performance metrics. Thus, it captures the essence of creating a more robust predictive framework that can be utilized across various AI-driven tasks.