Hi, What are you wondering? The individual tree is weakened by design (accepts more errors), so indeed, the individual trees are weak learners and the combination of them (the forest) becomes the strong learner. You can have a strong tree as well (deeper, more parameters), but that's not what is searched in a random forest.
Cheers, Matthieu Le dim. 16 août 2020 à 19:06, Fernando Marcos Wittmann <fernando.wittm...@gmail.com> a écrit : > > Hello guys, > > The the following reference states that Random Forests uses weak learners: > - > https://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-performance-metrics#:~:text=The%20random%20forest%20starts%20with,corresponds%20to%20our%20weak%20learner.&text=Thus%2C%20in%20ensemble%20terms%2C%20the,forest%20is%20a%20strong%20learner > >> The random forest starts with a standard machine learning technique called a >> “decision tree” which, in ensemble terms, corresponds to our weak learner. >> >> ... >> >> Thus, in ensemble terms, the trees are weak learners and the random forest >> is a strong learner. > > > I completely disagree with that statement. But I would like the opinion of > the community to double check if I am not missing something. > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn -- Quantitative researcher, Ph.D. Blog: http://blog.audio-tk.com/ LinkedIn: http://www.linkedin.com/in/matthieubrucher _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
