Boosting: meaning, definitions and examples

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boosting

 

[ ˈbuːstɪŋ ]

Noun
Context #1 | Noun

machine learning

Boosting is an ensemble learning technique that aims to combine the predictions of several base estimators in order to improve the accuracy of the model. It works by training a sequence of weak learners, where each learner corrects the mistakes made by its predecessor. The final prediction is then made by combining the predictions of all the weak learners.

Synonyms

AdaBoost, Gradient Boosting, ensemble learning

Examples of usage

  • Boosting algorithms such as AdaBoost and Gradient Boosting are popular in the machine learning community.
  • By iteratively adjusting the weights of misclassified samples, boosting can produce highly accurate models.
  • In boosting, the emphasis is on learning from the mistakes of previous models to improve overall performance.

Translations

Translations of the word "boosting" in other languages:

🇵🇹 impulsionar

🇮🇳 बढ़ावा देना

🇩🇪 verstärken

🇮🇩 meningkatkan

🇺🇦 підвищення

🇵🇱 wzmacnianie

🇯🇵 強化する (kyōka suru)

🇫🇷 amplifier

🇪🇸 impulsar

🇹🇷 güçlendirme

🇰🇷 증대 (jeungdae)

🇸🇦 تعزيز (ta'aziz)

🇨🇿 posílení

🇸🇰 posilnenie

🇨🇳 提升 (tíshēng)

🇸🇮 okrepitev

🇮🇸 styrking

🇰🇿 күшейту

🇬🇪 გაძლიერება (gazliereba)

🇦🇿 gücləndirmə

🇲🇽 impulsar

Etymology

The term 'boosting' in the context of machine learning originated in the 1990s as a method to enhance the performance of classification algorithms. The idea was inspired by the concept of boosting a weak learner into a strong learner by focusing on the misclassified examples. Over the years, boosting has become a fundamental technique in the field of machine learning, leading to the development of various boosting algorithms and frameworks.

See also: AdaBoost, boost, boosted, booster, reboost.

Word Frequency Rank

With rank #18,233, this word belongs to specialized vocabulary. While not common in everyday speech, it enriches your ability to express complex ideas.