Binary Classification#
Predicting one of two classes for each instance.
Important
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What it is#
Binary classification predicts one of two classes — positive/negative, 1/0, spam/not-spam. The model doesn’t output a bare label directly; it estimates the probability that an instance belongs to the positive class, then a decision threshold turns that probability into a hard label.
The threshold#
By default the cutoff is 0.5 — probability \(\ge 0.5\) → class 1, else class 0 — but 0.5 is not always right. On imbalanced data (e.g. fraud at 1%), 0.5 may label everything negative; the threshold is tuned against precision / recall or an ROC curve to match the cost of each error.
How it’s judged#
Predictions map to the confusion matrix — true and false positives and negatives — from which precision, recall, F1 and AUROC follow. The threshold choice moves directly along that trade-off.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
Classification Probability · Log-Odds · Sigmoid Function · Logistic Regression · Classification Models · Neural Networks
Hint
More in Classification & Averaging Metrics
Accuracy · AUC (Area Under the Curve) · Average Precision (AP) · 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
See also
Source article Adapted (context, re-expressed) in our own words from: Binary Classification (insightful-data-lab.com).