Model Score#
The raw numeric output a model assigns before thresholding.
Important
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What it is#
A model score is the continuous output a classifier assigns each example — a probability or real-valued score of belonging to the positive class — before it becomes a hard label. Logistic regression, random forests, and neural nets all emit scores.
Score vs label#
Turning a score into a decision requires a threshold — above it, positive; below, negative. The score carries more information than the label: its ranking (are positives scored above negatives?) is what threshold-free metrics like AUC measure, and its magnitude matters for ranking and prioritization.
Score vs probability#
A score need not be a calibrated probability — a score of 0.9 doesn’t guarantee a 90% chance of being positive unless the model is calibrated (e.g., via temperature or Platt scaling). Use the raw score for ranking, the calibrated one for decisions that need real probabilities.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
ROC Curve (Receiver Operating Characteristic) · AUC (Area Under the Curve) · Confidence Level · Log-Odds · Sigmoid Function · Temperature Scaling
Hint
More in Classification & Averaging Metrics
Accuracy · AUC (Area Under the Curve) · Average Precision (AP) · Binary Classification · Classification Probability · Discriminatory Power · F1-score · Gini Coefficient · Harmonic Mean · Log Loss (also called Logarithmic Loss or Cross-Entropy Loss) · Macro AUC · Macro AUROC (Macro-Averaged AUROC) · Macro Averaging · Macro F1
See also
Source article Adapted (context, re-expressed) in our own words from: Model Score (insightful-data-lab.com).