scikit-learn.org · ML Frameworks
Python's standard library for classical machine learning: one fit/predict interface for linear models, trees, clustering, preprocessing, cross validation and metrics.
scikit-learn is one of 20 ML framework tools on Axiomi. See all ML framework tools or how we check facts.
// guides
The best ML frameworks for Python in 2026, ranked by job: PyTorch, scikit-learn, JAX, Keras 3, XGBoost, LightGBM, TensorFlow, MLX and more, with versions.
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XGBoostxgboost.readthedocs.ioGradient boosted decision tree library for tabular data, with a scikit learn style Python API and CUDA-accelerated training and prediction.ML FrameworksOpen source★ 28.9k
LightGBMlightgbm.readthedocs.ioMicrosoft's tree based gradient boosting framework, designed for fast training and low memory use, with parallel, distributed and GPU learning.ML FrameworksOpen source★ 18.9k
PyTorchpytorch.orgThe dominant deep learning framework in research, with eager execution, torch.compile and a huge ecosystem.ML FrameworksOpen source★ 105.1k
Keraskeras.ioHigh level deep learning API that runs on JAX, TensorFlow or PyTorch backends.ML FrameworksOpen source★ 64.4k
Diffusershuggingface.coHugging Face's Python library of pipelines and building blocks for diffusion and flow models, covering image, video and audio generation, with LoRA training support.ML FrameworksOpen source★ 34.8k
Ollamaollama.comOpen source local model runner with a one line pull and run CLI, a desktop app and a local HTTP API. Uses llama.cpp-based and MLX engines; optional paid cloud models.ML FrameworksFreemium★ 185.1k