(i) Identify and extract mean reversion, (swing points) data points from non-stationary data, (ii) generate interpretable rules to predict such data points (iii) using supervised machine learning classification models in R such as GBM and RF.
inTrees (interpretable trees) is a framework for extracting, measuring, pruning, selecting and summarizing rules from a tree ensemble (so far including random forest, RRF and gbm). All algorithms for classification, and some for regression have been implemented in the "inTrees" R package. For Latex user: these rules can be easily formatted as latex code.
##Stack:
- EasyLanguage (C++)
- T-SQL (MS SQL Server 2016)
- R
- inTrees - The framework used to extract rules from tree ensembles
- Random GLM - Highly interpretable GLM ensembles