Classification Probability#
The probability a model assigns to a class for an instance.
Important
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What it is#
The classification probability is the score a classifier assigns that an instance belongs to a class — an estimate of \(P(\text{class} \mid \text{features})\) between 0 and 1. It is the model’s confidence before any hard decision is made.
From probability to label#
A threshold converts it to a class (in scikit-learn, predict_proba gives the probability,
predict applies the cutoff). Two instances scored 0.51 and 0.99 both become “positive,” but they are
not equally certain — which is why the probability carries more information than the label.
Why calibration matters#
The probability is only trustworthy if it is calibrated — if events predicted at 0.7 actually happen about 70% of the time. Over- or under-confident scores mislead any downstream risk-based decision, so probabilities are validated with calibration curves, not just accuracy.
Theme: Classification & Averaging Metrics · All terminology
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Mind map — connected ideas
Binary Classification · Log-Odds · Sigmoid Function · Logistic Regression · Probabilistic Forecasts · Multiclass AUROC
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More in Classification & Averaging Metrics
Accuracy · AUC (Area Under the Curve) · Average Precision (AP) · Binary Classification · 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 · Macro Precision
See also
Source article Adapted (context, re-expressed) in our own words from: Classification Probability (insightful-data-lab.com).