Bagging: meaning, definitions and examples
๐
bagging
[ หbรฆษกษชล ]
machine learning
Bagging, an abbreviation for Bootstrap Aggregating, is an ensemble machine learning technique that improves the stability and accuracy of algorithms. It does this by combining the predictions of multiple models to reduce variance and prevent overfitting.
Synonyms
bootstrap aggregation, ensemble learning
Examples of usage
- Bagging can significantly enhance model accuracy.
- Data scientists often use bagging to improve predictive performance.
- In bagging, multiple samples of the dataset are drawn to train different models.
collecting items
To bag means to put items into a bag or to collect something together in a more manageable format. This term is often used in contexts like shopping or gathering materials.
Synonyms
Examples of usage
- Don't forget to bag the groceries before leaving the store.
- He spent the afternoon bagging leaves in the yard.
- They are bagging donations for the charity event.
Translations
Translations of the word "bagging" in other languages:
๐ต๐น bagging
๐ฎ๐ณ เคฌเฅเคเคฟเคเค
๐ฉ๐ช Bagging
๐ฎ๐ฉ pengemasan
๐บ๐ฆ ัะฟะฐะบะพะฒะบะฐ
๐ต๐ฑ pakowanie
๐ฏ๐ต ใใฎใณใฐ
๐ซ๐ท bagging
๐ช๐ธ empaquetado
๐น๐ท poลetleme
๐ฐ๐ท ๋ฐฑํน
๐ธ๐ฆ ุชุนุจุฆุฉ
๐จ๐ฟ balenรญ
๐ธ๐ฐ balenie
๐จ๐ณ ๅ ่ฃ
๐ธ๐ฎ pakiranje
๐ฎ๐ธ pรถkkun
๐ฐ๐ฟ าะฐะฟัะฐั
๐ฌ๐ช แฉแแแแแแแก
๐ฆ๐ฟ paketleme
๐ฒ๐ฝ empaquetado
Word origin
The term 'bagging' originates from the 14th century Middle English word 'bagge,' which means a sack or pouch. The word evolved as the use of bags became prominent in transporting goods. In the realm of machine learning, the term was introduced in the 1990s as a significant data analysis technique, combining the concepts of bootstrap sampling and aggregation. Its purpose was to create more robust statistical models by reducing prediction variance through the technique of averaging results from multiple models. Bagging has since become a foundational approach in machine learning, associated with methods like Random Forests.