0.13.0 - 2025-07-15

API Changes

For Random Forest models, .out_of_bag_evaluations() now returns a
TrainingLogs object. The content is identical to the object previously
returned, but the number_of_trees property has been renamed to
iteration for consistency with Gradient Boosted Trees Training Logs.
mode="tf" is now the default on model.to_tensorflow_saved_model(). The
previous default is still available by setting mode="keras".
model.label() returns None for models trained without a label.
Remove deprecated evaluation_task argument for model.evaluate(). Use
task instead.

Add standalone C++ export with model.to_standalone_cc(). Standalone models
are super flexible, fast and memory-efficient. They only depend on the C++
standard library.
Add model.training_logs() method to return the training logs of the model.
Expose Mean Average Precision for Ranking tasks.
Add hyperparameters
numerical_vector_sequence_enable_closer_than_conditions and
numerical_vector_sequence_enable_projected_more_than_conditions.
Clear error messages when attempting to evaluate models without label.
Faster training with sparse oblique splits for datasets with many numerical
features
Many documentation improvements.
Increase default number of threads to 256 or number of CPU cores.
Enable cross-validation for hyperparameter tuning.
Add thresholds to classification plots.
Explicitly disable custom losses for hyperparameter tuning.
Disable parallel evaluation for cross-validation custom losses.

Distributed Training: recvmsg: Connection reset to isTransientError.
Enable SHAP values when training with BEST_FIRST_GLOBAL.
Predictions with cross-entropy LambdaMART no longer need the slow engine.
Disable the generic engine for oblique splits without global imputation.
This may fix a very rare bug in the way predictions are computed.

Sinfonie Nr. 4 in A-Dur, op. 90. Felix Mendelssohn