The Best metric-learn Alternatives in 2026

metric-learn provides Python implementations of metric learning algorithms.

numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.

This Scikit-learn extension accelerates compatible applications through optimized implementations.

Use Learnergy, a Python library for training and working with energy-based machine learning models.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

FEDOT automates machine learning model development through its modeling framework.

Enables single machine or distributed training and evaluation of deep learning models.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

Auto_ViML builds multiple machine learning models with a single line of code.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.

DeepSpeed optimizes distributed training and inference for deep learning models.

Guild AI automates and optimizes machine learning experiments while tracking each run.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.