Roadmap

Core

The list of items shown below are likely to be implemented due to their mainstream use, ability to increase learning speed, or ability to reduce computational resources.

• Online Decision Trees And Boosting Algorithms

  • Currently, the offline variants offer superior performance in terms of generalization for tabular datasets. However, because they tend to be computationally expensive and requires generally cannot perform incremental training, it is not suitable for real-time game environments.

  • The research literature on the online variants of these algorithms are lacking, and so we are waiting for more papers to come out.

  • Additionally, we lack experience in developing decision trees and boosting algorithms, which may result in long development times of these algorithms.

• Incremental DBSCAN

  • It is an online version of DBSCAN that allows it to construct clusters from individual datapoints.

Nice-To-Have

The list of items shown below may not necessarily be implemented in the future. However, they could be prioritized with external demand, collaboration, or funding.

• None