What are some common evaluation metrics used in classification and regression tasks?

In classification and regression tasks, evaluation metrics play a crucial role in assessing the performance and effectiveness of machine learning models. Different metrics are used depending on the nature of the task and the specific requirements of the problem at hand. Let's explore some common evaluation metrics used in classification and regression tasks: Classification Evaluation Metrics: 1. Accuracy: Accuracy is a commonly used metric that measures the proportion of correctly classified instances out of the total number of instances. It provides a general overview of model performance but may not be suitable for imbalanced datasets where class distributions are uneven. 2. Precision: Precision measures the proportion of correctly predicted positive instances (true positives) out of the total instances predicted as positive (true positives + false positives). Precision focuses on the model's ability to avoid false positives and is useful in situations where false positives are costly. 3. Recall (Sensitivity/True Positive Rate): Recall calculates the proportion of correctly predicted positive instances (true positives) out of the total actual positive instances (true positives + false ne....