Comments (2)
You get around 30% performance increase on predict(..., aggregation = 'average')
with this. Not a lot
from rforestry.
Actually the C++ compiler is much more powerful than I thought and this optimization makes basically no difference. Closing
from rforestry.
Related Issues (20)
- Getting weights variables HOT 1
- Clean up README for Python Package
- Training data column with only NAs
- `has_nas` method defined, but not used
- Export model to JSON string
- Add coverage report for unittests
- `seed` same in different objects HOT 3
- :no_good: Branch `bptest/test` has an incorrect name
- Implement Custom Sampling in Python
- Allow all NA or same value columns with scaling HOT 1
- Change linFeats to be 1-indexed instead of 0-indexed
- Python package documentation is outdated
- Fix authentication issue preventing python build pipeline to access docker image
- Build R package and upload it as pipeline artifact
- R release on CRAN - resolve std::cout?
- Add pipeline step for cross language tests / Python pipeline
- Floating point imprecision HOT 3
- Python Predictions not being rescaled when forest is trained with scale = True
- Compatibility with sklearn estimators HOT 6
- segmentation faults when splitratio = 0.5
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from rforestry.