What is the primary function of the inference process in machine learning?

The primary function of the inference process in machine learning is to predict outcomes from new data points. After a model has been trained on a set of existing data, it learns the underlying patterns and relationships. The inference phase utilizes this trained model to make predictions or decisions based on previously unseen data. This is crucial for applying the model in real-world scenarios, where it can provide valuable insights or predictions based on new input data.

Training the model involves adjusting its parameters and is a separate phase that occurs before inference. Evaluating model performance is typically done on validation or test sets to assess how well the model is likely to perform in production, but it does not involve making predictions on new instances. Data preprocessing is an essential step that prepares data for training but does not directly relate to how the model performs on new data during the inference step. Thus, the correct understanding of inference centers on its role in predicting outcomes, highlighting its significance in the application of machine learning models.