Data Science and Machine Learning Metrics

This page is a quick reference for common metrics across tasks like classification, regression, and clustering. Each entry includes a definition, when to use it, and links to an explanation (mostly from Wikipedia) and the relevant scikit-learn doc. This list is not meant to be exhaustive.

Metrics explained

A metric is a number that measures model performance, or how well predictions match actual outcomes.

• In supervised learning, metrics evaluate prediction quality (e.g. RMSE, F1).
• In unsupervised learning, they assess structure or similarity (e.g. silhouette score).
• During training, metrics guide choices like model selection and early stopping.

Related terms

• Loss function: What the model optimizes during training.
• Metric: What you monitor to evaluate results.
• Error metric: Often used in regression to describe prediction error.

Metrics tables

Classification

Metric	Code	Details
Accuracy	skl	Accuracy. Proportion of correct predictions to total predictions. Simple and intuitive. Can be very misleading for imbalanced classes.
Precision	skl	Precision. True Positives / (True Positives + False Positives). How many predicted positives are correct.
Recall	skl	Recall. True Positives / (True Positives + False Negatives). How many actual positives were captured.
F1	skl	F1 Score. Harmonic mean of precision and recall. Good for imbalanced classes.
ROC AUC	skl	ROC AUC. Area under the ROC curve. Evaluates ranking performance across thresholds.
PR AUC	skl	PR AUC. Area under the Precision-Recall curve. Better than ROC AUC for rare positives.
Log Loss	skl	Logarithmic Loss. Penalizes confident wrong predictions. Common in probabilistic classifiers.
Balanced Acc	skl	Balanced Accuracy. Mean recall across classes. Helps with imbalanced classes.
MCC	skl	Matthews Correlation Coefficient Balanced score even for class imbalance. Based on confusion matrix.

Regression

Metric	Code	Details
MSE	skl	Mean Squared Error. Average squared difference between predictions and true values. Penalizes larger errors more; sensitive to outliers. Not in original units.
RMSE	skl	Root Mean Squared Error. Same as MSE but in the original unit scale; easier to interpret. Still sensitive to outliers.
MAE	skl	Mean Absolute Error. Average absolute difference between predictions and actual values. More robust to outliers than MSE.
R²	skl	Coefficient of Determination. Measures proportion of variance explained by the model. Can be negative.
Adj R²		Adjusted R². Like R² but penalizes for additional predictors. Helps avoid overfitting.
MSLE	skl	Mean Squared Log Error. MSE on log-transformed targets. Good for targets spanning orders of magnitude.
MAPE	skl	Mean Absolute Percentage Error. Average of absolute percentage errors. Can blow up if targets are near zero.
SMAPE		Symmetric MAPE. Like MAPE but less sensitive to small denominators. Often used in time series.