What is used to assess the skill and performance of a machine learning model?

The use of evaluation metrics is fundamental when assessing the skill and performance of a machine learning model. Evaluation metrics provide quantitative measures that help determine how well a model is performing in relation to its intended purpose. These metrics can include accuracy, precision, recall, F1-score, ROC-AUC, and many others, depending on the type of problem being solved, such as classification or regression.

By relying on evaluation metrics, data scientists can systematically compare different models, tune hyperparameters, and decide which model to deploy in a production environment. The goal is to ensure that the chosen model generalizes well to new, unseen data, rather than simply fitting to the training data.

Other terms such as performance indicators, accuracy rates, and assessment criteria do relate to the evaluation process, but they are either too broad or incomplete in defining the specific measures used to quantitatively assess model performance. Evaluation metrics is the specific terminology that encapsulates the various calculations and comparisons used to measure a machine learning model's effectiveness.