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EvalML: a library for automated machine learning and model understanding

EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python

16 de May de 20212 de April de 2021

Machine Learning From Scratch

An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.

30 de May de 2020

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• Generative AI to quantify uncertainty in weather forecasting
  29 de March de 2024
  Posted by Lizao (Larry) Li, Software Engineer, and Rob Carver, […]
• AutoBNN: Probabilistic time series forecasting with compositional bayesian neural networks
  28 de March de 2024
  Posted by Urs Köster, Software Engineer, Google Research Time series […]
• Computer-aided diagnosis for lung cancer screening
  20 de March de 2024
  Posted by Atilla Kiraly, Software Engineer, and Rory Pilgrim, Product […]

• More productive, more sovereign: the case for an EU SME digitalisation voucher scheme
  23 de June de 2026
  The post More productive, more sovereign: the case for an EU SME digitalisation voucher scheme appeared first on European DIGITAL SME Alliance.

• Tuning Language Models by Mixture-of-Depths Ensemble
  26 de June de 2026
  arXiv:2410.13077v2 Announce Type: replace-cross Abstract: Transformer-based Large Language Models (LLMs) traditionally rely on final-layer loss for finetuning and final-layer representations […]
• Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
  26 de June de 2026
  arXiv:2601.16632v4 Announce Type: replace-cross Abstract: Time series forecasting has witnessed significant progress with deep learning. While prevailing approaches enhance forecasting […]
• Efficient foundation decoders for fault-tolerant quantum computing
  26 de June de 2026
  arXiv:2606.27119v1 Announce Type: cross Abstract: Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, […]