Evaluation Metrics resources
Evaluation metrics are quantitative measures used to assess the performance of a model or system. They are commonly used in machine learning, data science, and other fields to evaluate the accuracy, effectiveness, and efficiency of a particular model or system. Evaluation metrics can vary depending on the type of problem being solved, the data being used, and the goals of the project. Some common evaluation metrics include accuracy, precision, recall, F1 score, mean squared error, and area under the curve. These metrics can be used to compare different models or systems, to tune parameters, and to determine the overall effectiveness of a particular approach. There are many resources available online that discuss evaluation metrics and provide guidance on how to choose and use them effectively.