👉 Evaluated computing, also known as computational evaluation or model evaluation, is the systematic process of assessing the performance and accuracy of machine learning models or algorithms. It involves comparing the model's predictions against known outcomes, often using metrics like accuracy, precision, recall, F1 score, or mean squared error, to quantify how well the model generalizes from training data to unseen data. This evaluation is crucial for identifying strengths and weaknesses, guiding model improvements, and ensuring that the model meets the desired performance standards for its intended application. Evaluated computing helps in making informed decisions about model selection, hyperparameter tuning, and deployment strategies, ultimately contributing to the development of robust and reliable AI systems.
