Merge pull request #321 from stanfordmlgroup/version-bump-0.4.1

release partial fit 0.4.1

RELEASE_NOTES.md

@@ -1,5 +1,14 @@
 # RELEASE NOTES
 
+## Version 0.4.1
+### Added `partial_fit` method for incremental learning
+
+NGBoost now includes a new `partial_fit` method that allows for incremental learning. This method appends new base models to the existing ones, which can be useful when new data becomes available over time or when the data is too large to fit in memory all at once.
+
+The `partial_fit` method takes similar parameters to the `fit` method, including predictors `X`, outcomes `Y`, and validation sets `X_val` and `Y_val`. It also supports custom weights for the training and validation sets, as well as early stopping and custom loss monitoring.
+
+Please note that the `partial_fit` method is not yet fully tested and may not work as expected in all cases. Use it with caution and thoroughly test its behavior in your specific use case before relying on it in production.
+
 ## Version 0.4.0
 
 * Added support for the gamma distribution

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "ngboost"
-version = "0.4.0dev"
+version = "0.4.1dev"
 description = "Library for probabilistic predictions via gradient boosting."
 authors = ["Stanford ML Group <[email protected]>"]
 readme = "README.md"