Confusion Matrix & Metrics Cheat Sheet

How to read a confusion matrix and compute precision, recall, F1, and accuracy for binary and multi-class classification with scikit-learn.

1 PageBeginnerMar 15, 2026

Confusion Matrix & Report

Build and visualize a confusion matrix.

python

from sklearn.metrics import confusion_matrix, classification_report, ConfusionMatrixDisplayimport matplotlib.pyplot as plty_true = [1, 0, 1, 1, 0, 1, 0, 0]y_pred = [1, 0, 0, 1, 0, 1, 1, 0]cm = confusion_matrix(y_true, y_pred)print(cm)# [[TN FP]#  [FN TP]]print(classification_report(y_true, y_pred, target_names=["neg", "pos"]))ConfusionMatrixDisplay(cm, display_labels=["neg", "pos"]).plot()plt.show()

Metric Formulas

How each metric is derived from TP/TN/FP/FN.

python

# TP = true positive, TN = true negative# FP = false positive (Type I error), FN = false negative (Type II error)accuracy    = (TP + TN) / (TP + TN + FP + FN)precision   = TP / (TP + FP)        # of predicted positives, how many correctrecall      = TP / (TP + FN)        # of actual positives, how many found (sensitivity)specificity = TN / (TN + FP)        # of actual negatives, how many foundf1_score    = 2 * (precision * recall) / (precision + recall)

Metric Definitions

Core classification evaluation terms.

True Positive (TP)- model correctly predicts the positive class
True Negative (TN)- model correctly predicts the negative class
False Positive (FP)- model predicts positive but actual is negative (Type I error)
False Negative (FN)- model predicts negative but actual is positive (Type II error)
Precision- TP / (TP + FP); how trustworthy positive predictions are
Recall (Sensitivity)- TP / (TP + FN); how many actual positives were caught
F1 Score- harmonic mean of precision and recall; good for imbalanced classes
Accuracy- overall fraction correct; can be misleading on imbalanced datasets

Which Metric to Prioritize

Choosing metrics based on the cost of errors.

High cost of false positives- optimize for precision (e.g. spam filtering)
High cost of false negatives- optimize for recall (e.g. cancer screening)
Balanced classes, balanced costs- accuracy is a reasonable single-number summary
Imbalanced classes- prefer F1, precision-recall curves, or balanced accuracy over raw accuracy
Multi-class problems- use macro/micro/weighted averages of precision, recall, and F1

Pro Tip

On imbalanced datasets, accuracy can look great while the model just predicts the majority class every time — always check precision, recall, and F1 for the minority class, or look at the confusion matrix directly.

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