Hello,
Thanks for reaching out to us! The right place for ML.NET related question is the GitHub forum here, and the Gitter community. Sorry for the inconveniences.
Regards,
Yutong
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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?
Hello,
Thanks for reaching out to us! The right place for ML.NET related question is the GitHub forum here, and the Gitter community. Sorry for the inconveniences.
Regards,
Yutong