📏 Accuracy

Accuracy#

The fraction of predictions that are correct.

Important

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What it is#

Accuracy is the simplest classification metric — the fraction of predictions that are correct:

\[\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}.\]

It answers what share did the model get right?

The imbalance trap#

Accuracy is misleading on imbalanced data — if 99% of cases are negative, a model that predicts “negative” for everything scores 99% while catching zero positives. That false sense of security is why fraud, disease and anomaly tasks report precision / recall, AUC or F1 instead.

When it’s fine#

Accuracy is a reasonable headline when classes are roughly balanced and every error costs about the same. Otherwise it hides which errors happen — a confusion matrix and threshold-aware metrics tell the real story.

Theme: Classification & Averaging Metrics · All terminology

Hint

Mind map — connected ideas

Precision (a.k.a. Positive Predictive Value, PPV) · ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC) · Binary Classification · Multiclass Classification · Macro AUC · Precision–Recall AUC (PR-AUC)

Hint

More in Classification & Averaging Metrics

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 · Macro Precision