Overconfidence and Underconfidence Explained
When a model makes predictions in the form of probabilities, you expect those probabilities to reflect the true likelihood of an event. However, models can be overconfident or underconfident in their predictions. Overconfidence means the model assigns probabilities that are too high compared to the actual frequency of correct predictions. For example, if a model predicts 90% chance of positive but is only right 70% of the time when it says 90%, it is overconfident. Underconfidence is the opposite: the model's predicted probabilities are too conservative, so it predicts lower probabilities than the actual chance of being correct. For instance, if the model predicts 60% chance of positive but is actually correct 80% of the time at that level, it is underconfident. Recognizing these patterns is crucial for interpreting model outputs and improving calibration.
123456789101112131415import numpy as np # Example predicted probabilities and true labels predicted_probs = np.array([0.95, 0.8, 0.7, 0.4, 0.3, 0.1]) true_labels = np.array([1, 1, 0, 1, 0, 0]) # Compute average confidence (mean of predicted probabilities) average_confidence = np.mean(predicted_probs) # Compute accuracy (fraction of correct predictions using 0.5 threshold) predicted_classes = (predicted_probs >= 0.5).astype(int) accuracy = np.mean(predicted_classes == true_labels) print(f"Average confidence: {average_confidence:.2f}") print(f"Accuracy: {accuracy:.2f}")
Looking at the results, if the average confidence is significantly higher than the accuracy, this suggests the model is overconfident: it is more certain than it should be. If the average confidence is noticeably lower than the accuracy, the model is underconfident, meaning it is less certain than it should be. Properly calibrated models have average confidence close to their actual accuracy, ensuring the probabilities are trustworthy for decision-making.
1. How would overconfidence appear in a model's probability outputs?
2. What does underconfidence imply about a model's predictions?
3. Fill in the blank
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When a model makes predictions in the form of probabilities, you expect those probabilities to reflect the true likelihood of an event. However, models can be overconfident or underconfident in their predictions. Overconfidence means the model assigns probabilities that are too high compared to the actual frequency of correct predictions. For example, if a model predicts 90% chance of positive but is only right 70% of the time when it says 90%, it is overconfident. Underconfidence is the opposite: the model's predicted probabilities are too conservative, so it predicts lower probabilities than the actual chance of being correct. For instance, if the model predicts 60% chance of positive but is actually correct 80% of the time at that level, it is underconfident. Recognizing these patterns is crucial for interpreting model outputs and improving calibration.
123456789101112131415import numpy as np # Example predicted probabilities and true labels predicted_probs = np.array([0.95, 0.8, 0.7, 0.4, 0.3, 0.1]) true_labels = np.array([1, 1, 0, 1, 0, 0]) # Compute average confidence (mean of predicted probabilities) average_confidence = np.mean(predicted_probs) # Compute accuracy (fraction of correct predictions using 0.5 threshold) predicted_classes = (predicted_probs >= 0.5).astype(int) accuracy = np.mean(predicted_classes == true_labels) print(f"Average confidence: {average_confidence:.2f}") print(f"Accuracy: {accuracy:.2f}")
Looking at the results, if the average confidence is significantly higher than the accuracy, this suggests the model is overconfident: it is more certain than it should be. If the average confidence is noticeably lower than the accuracy, the model is underconfident, meaning it is less certain than it should be. Properly calibrated models have average confidence close to their actual accuracy, ensuring the probabilities are trustworthy for decision-making.
1. How would overconfidence appear in a model's probability outputs?
2. What does underconfidence imply about a model's predictions?
3. Fill in the blank
Danke für Ihr Feedback!
