Model Evaluation Metrics: A Comprehensive Guide
Last updated August 9, 2024
Evaluating the performance of your AI models is crucial for ensuring they meet your desired accuracy and reliability. Choosing the right evaluation metrics helps you understand how well your model performs in predicting future outcomes. This guide will explore key metrics used in model evaluation within Datrics AI Analyst Builder.
Common Model Evaluation Metrics
- Accuracy: The proportion of correctly classified predictions. It's a simple and widely used metric, but not always reliable for imbalanced datasets.
- Precision: The proportion of true positive predictions out of all positive predictions. It measures how accurate your positive predictions are.
- Recall (Sensitivity): The proportion of true positive predictions out of all actual positive examples. It measures how well your model captures true positive cases.
- F1-Score: The harmonic mean of precision and recall. It provides a balanced measure of both precision and recall, useful for evaluating models where both metrics are important.
- ROC Curve and AUC: The receiver operating characteristic (ROC) curve plots the true positive rate against the false positive rate for different classification thresholds. The area under the curve (AUC) represents the overall performance of the model.
- Mean Squared Error (MSE): Measures the average squared difference between predicted and actual values for regression problems.
- Root Mean Squared Error (RMSE): The square root of MSE, expressing errors in the same units as the target variable.
- R-squared: Indicates the proportion of variance in the target variable
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