Monitoring Model Performance Metrics
Last updated September 6, 2024
Keeping a close eye on your machine learning model's performance is essential to ensure its accuracy and effectiveness over time. Evidently AI simplifies this process by providing a comprehensive suite of tools to monitor and analyze key performance metrics.
Key Model Performance Metrics
- Accuracy: This metric measures the overall percentage of correct predictions your model makes. While a high accuracy is generally desirable, it might not be the best indicator for all situations, especially when dealing with imbalanced datasets.
- Precision: Precision measures the proportion of positive predictions that are actually correct. It indicates how reliable your model is at identifying true positives.
- Recall: Recall, also known as sensitivity, measures the proportion of actual positive cases that your model correctly identifies. It tells you how well your model captures all the relevant instances.
- F1-Score: The F1-score is a harmonic mean of precision and recall. It provides a balanced measure of your model's accuracy, considering both precision and recall.
- ROC AUC: The Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate at various threshold settings. The area under the ROC curve (AUC) represents the overall performance of your classifier, indicating its ability to distinguish between positive and negative cases.
- Log Loss: Log loss (also known as cross-entropy) is a metric used to measure the performance of probabilistic classification models. It penalizes more strongly for incorrect predictions that are more confident. Lower log loss values indicate better performance.
- Mean Absolute Error (MAE): MAE measures the average absolute difference between your model's predictions and the actual values. It's commonly used in regression tasks.
- Mean Squared Error (MSE): MSE calculates the average squared difference between predictions and actual values. It's sensitive to outliers and can be used in regression tasks.
- R-squared: R-squared, sometimes called the coefficient of determination, measures how well a regression model fits the data. Values closer to 1 indicate a better fit.
- Custom Metrics: You can also define and monitor your own custom metrics that are specific to your domain or application.
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