![]() ![]() Meta typically means raising the level of abstraction one step and often refers to information about something else.įor example, you are probably familiar with “ meta-data,” which is data about data. Model Selection and Tuning as Meta-Learning.Meta-Algorithms, Meta-Classifiers, and Meta-Models.This tutorial is divided into five parts they are: Meta-learning also refers to algorithms that learn how to learn across a suite of related prediction tasks, referred to as multi-task learning.Meta-learning algorithms typically refer to ensemble learning algorithms like stacking that learn how to combine the predictions from ensemble members.Meta-learning refers to machine learning algorithms that learn from the output of other machine learning algorithms.In this tutorial, you will discover meta-learning in machine learning.Īfter completing this tutorial, you will know: ![]() It also refers to learning across multiple related predictive modeling tasks, called multi-task learning, where meta-learning algorithms learn how to learn. Nevertheless, meta-learning might also refer to the manual process of model selecting and algorithm tuning performed by a practitioner on a machine learning project that modern automl algorithms seek to automate. Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning. Meta-learning in machine learning refers to learning algorithms that learn from other learning algorithms. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |