scikit-learn-contrib/metric-learn

Health v2 · maintenance only

Adoption not part of the score

1438 stars · 231 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

metric-learn is a Python library providing efficient implementations of popular supervised, weakly-supervised, and unsupervised metric learning algorithms such as LMNN, ITML, NCA, and LFDA. As part of scikit-learn-contrib, its API is fully compatible with scikit-learn, enabling use in pipelines and model selection routines.

Use cases

• learn a Mahalanobis distance metric to improve k-NN classification accuracy
• embed data with Neighborhood Components Analysis before clustering
• train a supervised metric from labeled pairs or triplets
• improve nearest-neighbor retrieval with a learned distance
• plug a metric learner into a scikit-learn pipeline with cross-validation
• compare metric learning algorithms like LMNN, ITML, and SDML on a dataset

When to choose

• you need learned distance metrics that integrate with scikit-learn pipelines and model selection
• you want a well-documented collection of classic metric learning algorithms under an MIT license
• you are doing research or prototyping in similarity or representation learning with Python

When to avoid

• you need deep metric learning with neural networks or GPU acceleration
• your project requires actively developed features on the latest scikit-learn versions
• you only need standard distances like Euclidean or cosine without learning

Facets

library · maturity maintenance

machine-learning machine-learning data-science python metric-learning scikit-learn distance-metrics mahalanobis similarity-learning

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