Bagging

TECHNICAL DEFINITION

Bagging (Bootstrap Aggregating) is an ensemble meta-algorithm that trains multiple instances of the same base learning algorithm on different bootstrap samples of the training data, then aggregates their predictions (e.g., averaging for regression, voting for classification) to reduce variance.

BACKGROUND

A large language model (LLM) is a neural network trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts, and are a foundational technology behind modern chatbots. Biased or inaccurate training data can make an LLM's output less reliable.

SYNONYMS & ALIASES

• Bootstrap aggregating
• ensemble learning
• model averaging

USAGE NOTE

Random Forests are a popular example of an algorithm that utilizes bagging.

DEVELOPERS

Organizations developing technology related to Bagging.

• H2O.ai

  H2O.ai's Driverless AI platform automates machine learning, including model selection and optimization, where ensemble methods like bagging are intrinsically used to build robust models, directly aiding AI engineers in developing high-quality systems.

• DataRobot

  DataRobot's AutoML platform significantly helps AI engineers by automating the entire modeling pipeline, which includes the intelligent application of various ensemble techniques such as bagging to achieve best-in-class model performance and stability.

• Google (Vertex AI AutoML)

  Google's Vertex AI provides AutoML services that allow AI engineers to automatically train and deploy high-quality models. These services often leverage ensemble methods, including bagging, to improve model accuracy and robustness without manual hyperparameter tuning.

• Microsoft (Azure Machine Learning AutoML)

  Azure ML offers AutoML capabilities that streamline the model development process for AI engineers. It intelligently explores various algorithms and ensemble methods like bagging to produce robust, production-ready models.

• Amazon (SageMaker Autopilot)

  Amazon SageMaker Autopilot automates model building for AI engineers, efficiently exploring different machine learning techniques, including the application of ensemble methods like bagging, to deliver the best predictive performance for a given dataset.

• Databricks

  Databricks provides a unified data and AI platform, including MLflow, which facilitates the entire ML lifecycle. Their platforms support and enable AI engineers to implement and operationalize robust models, often leveraging ensemble techniques like bagging for improved stability and performance.

• Domino Data Lab

  Domino provides an MLOps platform that empowers AI engineers to develop, deploy, and manage machine learning models at scale. While not directly creating bagging algorithms, their platform provides the computational environments and tools that allow engineers to effectively utilize and operationalize ensemble methods for more reliable AI systems.