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H2O v3.26.0.11 Release Notes

Release Date: 2019-05-12 // almost 5 years ago

  • 🚀 Download at: http://h2o-release.s3.amazonaws.com/h2o/rel-yau/11/index.html

    Bug

    👍 [PUBDEV-6580] - The Python client now fails with descriptive message when attempting to run on an unsupported Java version. [PUBDEV-6895] - Fixed an issue that caused h2o to fail when running on Hadoop with -internal_secure_connections. [PUBDEV-6911] - H2OGenericEstimator can now be instantiated with no parameters. [PUBDEV-6945] - Multi-node H2O XGBoost now returns reproducible results. 0️⃣ [PUBDEV-6995] - Fixed the backend default values for the inflection_point and smoothing parameters in Target Encoder. [PUBDEV-7006] - Users can now specify the noise parameter when running Target Encoding in the R client or in Flow. ⚠ [PUBDEV-7036] - MOJO reader now uses stderr instead of stdout to show warnings. 🛠 [PUBDEV-7056] - Fixed an issue that allowed SPNEGO athentication to pass with any HTTP-Basic header. [PUBDEV-7062] - When connecting to H2O via the Python client, users can now specify allowed_properties="cacert".

    New Feature

    [PUBDEV-6213] - Added BroadcastJoinForTargetEncoding.

    Task

    [PUBDEV-6970] - Introduced AllCategorical and Threshold TE application strategies.

    Improvement

    ✅ [PUBDEV-7052] - Added a test to check XGBoost variable importance when trained on frames with shuffled input columns. 📦 [PUBDEV-7053] - The package name for ai.h2o.org.eclipse.jetty.jaas.spi is now independent of the Jetty version. [PUBDEV-7060] - The offset_column is now propogated to MOJO models.

    📄 Docs

    📚 [PUBDEV-7070] - Improved documentation for stopping_metric as it pertains to AutoML.