GITNUX MARKETDATA REPORT 2023
Must-Know Model Evaluation Metrics
Highlights: The Most Important Model Evaluation Metrics
- 1. Accuracy
- 2. Precision
- 3. Recall (Sensitivity)
- 4. F1-score
- 5. Specificity
- 6. ROC-AUC (Receiver Operating Characteristic – Area Under the Curve)
- 7. PR-AUC (Precision-Recall Area Under the Curve)
- 8. Log Loss (Logarithmic Loss)
- 9. Mean Absolute Error (MAE)
- 10. Mean Squared Error (MSE)
- 11. Root Mean Squared Error (RMSE)
- 12. R-squared (Coefficient of Determination)
- 13. Adjusted R-squared
- 14. Confusion Matrix
- 15. Cohen’s Kappa
- 16. Matthews Correlation Coefficient (MCC)
Table of Contents
Model Evaluation Metrics: Our Guide
Navigating the complex world of model evaluation can be daunting. Our recent study delves into the must-know metrics that every aspiring data scientist or machine learning enthusiast should understand. Prepare yourself to gain in-depth knowledge about accuracy, precision, recall and many other critical benchmarks that hold the key to the accuracy of your prediction models.
The proportion of correctly classified instances out of the total instances. It works well when the classes are balanced but may be misleading when classes are imbalanced.
Measures the proportion of true positives out of total predicted positives. It indicates how well the model correctly identifies positive instances.
Measures the proportion of true positives out of total actual positives. It indicates how well the model identifies all relevant instances.
The harmonic mean of precision and recall. It provides a single value that balances both precision and recall and is especially helpful when dealing with imbalanced classes.
Measures the proportion of true negatives out of total actual negatives. It indicates how well the model identifies non-relevant instances.
Receiver Operating Characteristic
A plot of true positive rate (sensitivity) against false positive rate (I-specificity) at various threshold settings.
A plot of precision against recall at various threshold settings. It is particularly useful for imbalanced datasets, as it focuses on the minority class performance.
Measures the performance of a classifier by penalizing wrong predictions. It is suitable for multi-class problems and heavily penalizes confident incorrect predictions.
Mean Absolute Error
The average difference between the actual values and the predicted values. It measures the prediction error magnitude and is robust to outliers.
Mean Squared Error
The average squared difference between the actual values and the predicted values. It measures the prediction error magnitude and is sensitive to outliers.
Root Mean Squared Error
The square root of MSE. It measures the prediction error magnitude and has the same units as the output, making interpretation easier.
R-Squared (Coefficient Of Determination)
Measures the proportion of variance in the dependent variable that is predictable from the independent variables. It indicates how well the model fits the data and ranges from 0 to 1.
A modified version of R-squared that accounts for the number of predictors in the model. It provides a more reliable assessment of the model’s performance when there are multiple predictors.
A table that shows the counts of true positives, true negatives, false positives, and false negatives, allowing for a more detailed analysis of a classifier’s performance.
A measure of agreement between two raters that accounts for the agreement that would happen purely by chance. It ranges from -1 to 1, where 1 indicates perfect.
Frequently Asked Questions
What are Model Evaluation Metrics and why are they important?
What are some common model evaluation metrics used in classification problems?
How does the Confusion Matrix play a role in measuring the performance of a classification model?
What is the difference between Mean Absolute Error (MAE) and Mean Squared Error (MSE) in evaluating regression models?
What are some model evaluation metrics specific to time series forecasting?
How we write these articles
We have not conducted any studies ourselves. Our article provides a summary of all the statistics and studies available at the time of writing. We are solely presenting a summary, not expressing our own opinion. We have collected all statistics within our internal database. In some cases, we use Artificial Intelligence for formulating the statistics. The articles are updated regularly. See our Editorial Guidelines.