What is the process of teaching AI models using specific data called?

The process of teaching AI models using specific data is known as model training. During this phase, the model learns patterns from the input data by adjusting its parameters to minimize the error in predictions or classifications it makes based on that data. This is a crucial step in the development of any machine learning model, as it directly influences how well the model will perform on unseen data.

Model training entails feeding the model large amounts of labeled data, which includes examples from which the model can learn. The adjustments made to the model during this training phase allow it to generalize from the training data to new, unseen scenarios, enhancing its predictive capabilities.

While the other listed options play important roles in the broader context of machine learning, they are distinct processes. Data mining refers to the exploration and analysis of large data sets to find patterns, algorithm optimization involves fine-tuning the model's algorithm for enhanced performance, and data preprocessing encompasses the steps taken to clean and format data before it is used for training. These processes lay the groundwork for effective model training but are not synonymous with it.