👉 Evaluations computing, also known as model evaluation or performance assessment, is the process of assessing and measuring how well a machine learning model performs on unseen data. This involves using various metrics to quantify the model's accuracy, precision, recall, F1 score, or other relevant measures depending on the task at hand. Evaluations computing typically includes splitting the dataset into training, validation, and test sets to ensure that the model generalizes well beyond the training data. Techniques like cross-validation and bootstrapping are often employed to enhance the reliability of these assessments. The ultimate goal is to identify strengths and weaknesses in the model, guide improvements, and ensure that it can effectively perform in real-world scenarios.
