LGBM Intuition to Non-technical individuals How can i explain LGBM to a non-technical person as it involves Trees/Ensembling and much more?
Using LGBM for solving a Regression problem and how does it helps in:


*

*Better Prediction

*Feature Importance distribution.

*Which hyper-parameters to focus more.



In general, boosting algorithms use smaller batches and new small trees learn from the errors of the previous small trees, thus gradually decrease the error and increase the accuracy. At the end they are ensembled.
More intuitive info in Wikipedia:
https://en.wikipedia.org/wiki/LightGBM
