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 negatives). It assesses the model's ability to capture all positive instances and is particularly relevant in scenarios where false negatives are costly.
4. F1 Score: The F1 score is the harmonic mean of precision and recall, providing a balanced measure that combines both metrics. It is useful when there is an imbalance between positive and negative instances or when precision and recall need to be considered simultaneously.
5. Specificity (True Negative Rate): Specificity measures the proportion of correctly predicted negative instances (true negatives) out of the total actual negative instances (true negatives + false positives). It indicates the model's ability to correctly identify negative instances and is particularly relevant when the focus is on minimizing false positives.
6. Area Under the Receiver Operating Characteristic Curve (AUC-ROC): AUC-ROC measures the performance of a binary classifier across different discrimination thresholds. It plots the true positive rate (sensitivity) against the false positive rate (1 - specificity) and calculates the area under the curve. A higher AUC-ROC value indicates better classifier performance, with a value of 0.5 representing random performance and 1 representing perfect classification.
7. Log Loss (Cross-Entropy Loss): Log loss calculates the logarithm of the likelihood of the predicted probabilities for each class. It is commonly used for probabilistic classifiers and penalizes incorrect predictions more heavily. Lower log loss values indicate better model performance.

Regression Evaluation Metrics:

1. Mean Absolute Error (MAE): MAE measures the average absolute difference between predicted and actual values. It provides a straightforward interpretation of the average prediction error, regardless of the direction of the error.
2. Mean Squared Error (MSE): MSE calculates the average squared difference between predicted and actual values. It amplifies larger errors compared to MAE, making it more sensitive to outliers.
3. Root Mean Squared Error (RMSE): RMSE is the square root of MSE and provides a metric in the same unit as the target variable. It is commonly used to assess the average magnitude of the prediction error.
4. R-squared (Coefficient of Determination): R-squared measures the proportion of the variance in the target variable explained by the model. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data. However, R-squared may not be suitable for complex models or when the number of predictors is large.
5. Mean Absolute Percentage Error (MAPE): MAPE calculates the average percentage difference between predicted and actual values, providing an assessment of relative errors. It is useful when the scale of the target variable varies widely.
6. Median Absolute Error (MedAE): MedAE