For my master thesis, I am using ML.Net library with the model builder to build machine learning models for regression / to predict values. My field of study is in constructional mechanics, so I'm rather new to machine learning.
For most of my models, tree-based regression algorithms seem to be the best performing, and of these LightGBM and FastTree are the best performing algorithms.
I've tried to read up on LightGBM here: https://www.microsoft.com/en-us/research/publication/lightgbm-a-highly-efficient-gradient-boosting-decision-tree/ And FastTree here: https://learn.microsoft.com/en-us/dotnet/api/microsoft.ml.trainers.fasttree.fasttreeregressiontrainer?view=ml-dotnet
However, I struggle to distinguish what the difference between these algorithms is. Could someone explain what the difference between LightGBM and FastTree is?