Model Evaluation Metrics

Model evaluation metrics are quantitative measures used to assess the performance of a machine learning model. These metrics provide insights into how well the model is generalizing to unseen data and help in comparing different models or tuning hyperparameters. They quantify various aspects of model behavior, such as accuracy, precision, recall, and error rate, allowing data scientists to make informed decisions about model selection and deployment.

Visit the following resources to learn more:

• @article@Metrics and scoring: quantifying the quality of predictions | scikit-learn