Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
LightGBM support pandas columns of category type.
As a matter of fact,
this is the suggested way of handling categorical columns in LightGBM.
data[feature] = pd.Series(data[feature], dtype="category")
A LightGBM model (which is a Booster object) records categories of each categorical feature. This information is used to set categories of each categorical feature during prediction, which ensures that a LightGBM model can always handle categorical features correctly.
Tips on LightGBM
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
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It is strongly suggested that you load data into a pandas DataFrame and handle categorical variables by specifying a
dtypeof"category"for those categorical variables.df.cat_var = df.cat_var.astype …
LightGBM on GPU
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://pypi.org/project/lightgbm/#build-gpu-version
https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version
https://www.kaggle.com/vinhnguyen/gpu-acceleration-for-lightgbm
Microsoft's Example Dockerfile for GPU version of LightGBM …
Use LightGBM With Spark
Things on this page are fragmentary and immature notes/thoughts of the author. Please read with your own judgement!
https://github.com/Azure/mmlspark/blob/master/docs/lightgbm.md
MMLSpark seems to be the best option to use train models using LightGBM on a Spark cluster. Note that MMLSpark requires …