In the context of machine learning, what does ‘parameter tuning’ refer to?

Parameter tuning specifically refers to the process of adjusting hyperparameters, which are the configuration values used to control the training process of a machine learning model before the training begins. These hyperparameters include settings like the learning rate, the number of trees in a random forest, the regularization strength, and many others. By fine-tuning these values, practitioners aim to optimize the learning process and improve model performance.

Setting hyperparameters is a critical step in the machine learning workflow, as these values can significantly influence how well the model learns from the data and generalizes to unseen data. The correct tuning of hyperparameters often involves various methods such as grid search, random search, or more advanced techniques like Bayesian optimization.

The other options refer to different concepts in machine learning. Adjusting model parameters during training relates to how the model learns from the training data, which is separate from hyperparameter tuning. Evaluating model performance is crucial for understanding how well a model works, but it happens after training. Visualizing training data is also important for understanding the data itself but does not pertain to tuning parameters.