bagging (bootstrap aggregating)

V obuvnickém průmyslu lze bagging využít k předvídání trendů prodeje a k přesnějším prognózám poptávky po konkrétních typech obuvi. Pokud by například výrobce obuvi použil metodu baggingu k sestavení více modelů z různých vzorců prodeje, mohl by lépe předvídat trendy a reagovat na změny v poptávce. Bagging lze také využít k identifikaci vad při výrobě obuvi, protože algoritmus může identifikovat vzory vad a pomoci zlepšit výrobní proces.

Το bootstrap aggregating, που ονομάζεται επίσης σακκόπανο(από το bootstrap aggregating), είναι ένας μετα-αλγόριθμος συνόλου μηχανικής εκμάθησης που έχει σχεδιαστεί για να βελτιώνει τη σταθερότητα και την ακρίβεια των αλγορίθμων μηχανικής μάθησης που χρησιμοποιούνται στη στατιστική ταξινόμηση και παλινδρόμηση. Μειώνει επίσης τη διακύμανση και βοηθά στην αποφυγή υπερβολικής προσαρμογής. Αν και συνήθως εφαρμόζεται σε μεθόδους δένδρων αποφάσεων, μπορεί να χρησιμοποιηθεί με οποιοδήποτε τύπο μεθόδου. Το σακκόπανο είναι μια ειδική περίπτωση της προσέγγισης του μέσου όρου του μοντέλου.

Bootstrap aggregating, detto anche bagging (da bootstrap aggregating), è un meta-algoritmo di apprendimento di insieme automatico progettato per migliorare la stabilità e l'accuratezza degli algoritmi di apprendimento automatico utilizzati nella classificazione e nella regressione statistica. Inoltre, riduce la varianza e aiuta a evitare l'overfitting. Sebbene sia solitamente applicato ai metodi ad albero decisionale, può essere utilizzato con qualsiasi tipo di metodo.

Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.

A agregação bootstrap, também conhecida como bagging, é um meta-algoritmo de aprendizagem automática concebido para melhorar a estabilidade e a precisão dos algoritmos de aprendizagem automática utilizados na classificação e regressão estatísticas. Também reduz a variância e ajuda a evitar o sobreajuste. Embora seja normalmente aplicado a métodos de árvore de decisão, pode ser utilizado com qualquer tipo de método. O ensacamento é um caso especial de cálculo da média dos modelos.

Agregarea bootstrap, numită și bagging (din agregarea bootstrap), este un meta-algoritm al ansamblului de învățare automată conceput pentru a îmbunătăți stabilitatea și acuratețea algoritmilor de învățare automată utilizați în clasificarea statistică și regresie. De asemenea, reduce varianța și ajută la evitarea supraadaptarii. Deși se aplică de obicei metodelor arborelui de decizie, poate fi folosit cu orice tip de metodă. Ambalarea este un caz special al abordării modelului de mediere.

Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.

La agregación bootstrap, también llamada bagging (de bootstrap aggregating), es un metaalgoritmo de aprendizaje automático diseñado para mejorar la estabilidad y precisión de los algoritmos de aprendizaje automático utilizados en la clasificación y regresión estadística. También reduce la varianza y ayuda a evitar el sobreajuste. Aunque suele aplicarse a los métodos de árboles de decisión, puede utilizarse con cualquier tipo de método. El bagging es un caso especial del método de promediado de modelos.

Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach.