Neural Network Lexicon

Short Definition

Model confidence expresses how certain a model is about its predictions.

Definition

Model confidence refers to the probability or score a model assigns to its predicted outcome. In classification tasks, this is often the predicted probability of the chosen class. Confidence is intended to reflect how strongly the model believes its prediction is correct.

However, confidence is a model-internal signal and does not automatically correspond to real-world correctness.

Why It Matters

In many applications, knowing how confident a model is matters as much as the prediction itself. High-confidence errors can be far more harmful than low-confidence ones.

Model confidence is critical in decision-making systems, risk-sensitive applications, and human-in-the-loop workflows.

How It Works (Conceptually)

• The model produces a score or probability for each class
• The highest score is selected as the prediction
• That score is interpreted as confidence
• Confidence is meaningful only if the model is well calibrated

Confidence reflects belief, not truth.

Minimal Python Example

prediction = max(class_probabilities)
confidence = prediction

Common Pitfalls

• Treating confidence as correctness
• Assuming neural network probabilities are reliable by default
• Ignoring confidence miscalibration
• Using confidence without evaluation or calibration

Related Concepts

• Calibration
• Reliability Diagrams
• Expected Calibration Error (ECE)
• Uncertainty Estimation
• Decision Thresholding