AI Model Metrics
Last updated
Last updated
AI metrics are essential tools for evaluating the performance of machine learning models, offering insights into their effectiveness and reliability. Accuracy measures the overall correctness of the model by calculating the proportion of correctly classified instances. Recall (or sensitivity) evaluates the model's ability to identify all relevant instances, making it particularly useful in detecting rare or critical cases. Specificity, on the other hand, assesses the model's ability to correctly reject irrelevant or negative instances, ensuring it avoids false positives. Additional metrics, such as precision, focus on the accuracy of positive predictions, while the F1-score balances precision and recall to provide a harmonic mean, especially useful when dealing with imbalanced datasets. Together, these metrics enable developers to fine-tune models for specific applications and optimize their real-world performance.
Key Metrics Explained:
Precision = True Positives / (True Positives + False Positives) Precision (positive predictive value) represents the fraction of relevant instances among the retrieved instances.
Recall (Sensitivity) = True Positives / (True Positives + False Negatives) Recall is the fraction of relevant instances that were successfully retrieved. Sensitivity (True Positive Rate) refers to the probability of a positive test given that the instance is truly positive.
Specificity (True Negative Rate) = True Negatives / (True Negatives + False Positives) Specificity measures the probability of a negative test result given that the instance is truly negative
In Csmart-Digit, AI models are evaluated using a range of metrics to assess their performance in both binary classification (defect detection) and multiclass classification (categorizing seeds by classes). These metrics provide critical insights into the effectiveness, reliability, and confidence of the model's predictions.
Key AI Metrics
Average Entropy Entropy measures the uncertainty in the model’s predictions. Low entropy indicates confident and consistent predictions, while high entropy suggests uncertainty. For instance, an entropy value of 40% may reflect some randomness in the model's outputs, requiring further refinement in training or data.
Inference Confidence Level This metric evaluates the confidence of the model in its predictions. Confidence levels are categorized based on entropy values:
High Confidence: Entropy < 12%
Medium Confidence: 12% ≤ Entropy < 20%
Low Confidence: 20% ≤ Entropy < 40%
Low Reliability: 40% ≤ Entropy < 75%
Very Low Reliability: 75% ≤ Entropy < 100%
Lower confidence levels highlight areas where the model may need improvement to enhance prediction reliability.
Cohen's Kappa The kappa coefficient measures agreement between predicted and actual labels, accounting for random chance. A value of 1.0 indicates perfect agreement, while values close to 0 suggest performance close to random guessing. High kappa values are crucial in ensuring both binary and multiclass predictions are accurate and reliable, particularly when dealing with imbalanced datasets. However, Cohen's Kappa will only produce results if the user provides feedback by correcting the model’s predictions within the software. This feedback is essential to calculate the agreement between the model's predictions and the actual corrected labels, enabling a meaningful kappa score.
Binary Accuracy and Binary Error
Binary Accuracy reflects the proportion of correctly classified instances in binary tasks, such as identifying defective or non-defective seeds. For example, a binary accuracy of 98% indicates strong performance in this task.
Binary Error indicates the rate of incorrect classifications in binary tasks. A low error rate ensures that defective beans are reliably identified.
Multiclass Accuracy and Multiclass Error
Multiclass Accuracy measures the proportion of correct classifications across all coffee categories, such as bean grades or flavor profiles. For example, a multiclass accuracy of 95% demonstrates effective classification across categories.
Multiclass Error represents the rate of misclassifications in multiclass tasks. A low error rate confirms the model's ability to distinguish between diverse coffee categories accurately.
Confusion Matrix A confusion matrix for multiclass classification illustrates the performance of a classification model. Each row represents the actual classes, and each column corresponds to the predicted (inferred) classes. The diagonal elements show correct predictions, while off-diagonal elements indicate errors. The sum of each row reflects the total instances of an actual class, and the sum of each column indicates the total predictions for a class. Examining the confusion matrix provides a clear understanding of where the model performs well and where errors occur.
