# Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
## 6 Discussion and Conclusions  
Forgetful decision trees and forgetful random forests constitute simple, fast and accurate incremental data structure algorithms. We have found that:  
* The forgetful decision tree is at least three times faster and at least as accurate as state-of-the-art incremental decision tree algorithms for a variety of concept drift datasets. When the precision, recall, and F1 score are appropriate, the forgetful decision tree has a similar F1 score to state-of-the-art incremental decision tree algorithms.
* The forgetful random forest without bagging is at least 24 times faster than state-of-the-art incremental random forest algorithms, but is less accurate by at most 2%.
* By contrast, the forgetful random forest with bagging has a similar accuracy to the most accurate state-of-the-art forest algorithm (adaptive random forest) and is 2.5 faster.
* At a conceptual level, our experiments show that it was possible to set hyperparameter values based on changes in accuracy on synthetic data and then apply those values to real data. The main such hyperparameters are \(iRate\) (increase rate of size of data retained), \(tThresh\) (the confidence interval that accuracy has changed), and \(nTree\) (the number of decision trees in the forgetful random forests).
* Further our experiments show the robustness of our approach across a variety of applications where concept drift is frequent or infrequent, mild or drastic, and gradual or abrupt.  
In summary, forgetful data structures speed up traditional decision trees and random forests for streaming data and help them adapt to concept drift. Further, bagging increases accuracy but at some cost in speed. The most pressing question for future work is whether some alternative method to bagging can be combined with forgetfulness to increase accuracy at less cost in a streaming concept drift setting.  
Conceptualization, Z.Y. and D.S.; methodology, Z.Y.; software, Z.Y.; validation, Z.Y. and Y.S.; formal analysis, Z.Y.; investigation, Z.Y.; resources, Z.Y. and D.S.; data curation, Z.Y.; writing--original draft preparation, Z.Y., Y.S. and D.S.; writing--review and editing, Z.Y., Y.S. and D.S.; visualization, Z.Y.; supervision, D.S.; project administration, D.S.; funding acquisition, D.S. All authors have read and agreed to the published version of the manuscript.  
Figure 6: F1 score of random forests: The forgetful random forest without bagging has a lower precision, recall, and F1 score (by 0.02) compared to the adaptive random forest. The forgetful random forest with bagging has a lower F1 score (by 0.01) but a similar precision to the adaptive random forest.  
**Funding:** The authors would like to acknowledge the support of the U.S. National Science Foundation grants 1840761, 1934388, and 1840761, the U.S. National Institutes of Health grant 5R01 GM121753 and NYU Wireless.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data and our code can be found here: [[https://github.com/ZhehuYuan/Forgetful-Random-Forest.git](https://github.com/ZhehuYuan/Forgetful-Random-Forest.git)]([https://github.com/ZhehuYuan/Forgetful-Random-Forest.git](https://github.com/ZhehuYuan/Forgetful-Random-Forest.git)) (accessed on 25 May 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.