In a random forest, how do we calculate the OOB error rate when observations appear in all trees in RF? - Quora
![Bootstrapping and the Role of Out-of-Bag Samples in Identifying Important Features in Random Forest Models | Kaggle Bootstrapping and the Role of Out-of-Bag Samples in Identifying Important Features in Random Forest Models | Kaggle](https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F11239873%2Fcdb7d0f2e846b6108f44d76034ddd156%2Fbootstrap.jpg?generation=1690620779159779&alt=media)
Bootstrapping and the Role of Out-of-Bag Samples in Identifying Important Features in Random Forest Models | Kaggle
![Random Forests based classification procedure for N trees grown. OOB... | Download Scientific Diagram Random Forests based classification procedure for N trees grown. OOB... | Download Scientific Diagram](https://www.researchgate.net/publication/236230903/figure/fig4/AS:613866337751062@1523368548604/Random-Forests-based-classification-procedure-for-N-trees-grown-OOB-stands-for.png)
Random Forests based classification procedure for N trees grown. OOB... | Download Scientific Diagram
![The bootstrap. m samples of the original data are produced by sampling... | Download Scientific Diagram The bootstrap. m samples of the original data are produced by sampling... | Download Scientific Diagram](https://www.researchgate.net/publication/350044924/figure/fig1/AS:1007763559809025@1617280964433/The-bootstrap-m-samples-of-the-original-data-are-produced-by-sampling-with-repetition.png)