Features

• Speed-up the computation of PDP and CEP in the model analysis tool.
• Add compilation of model into .h file.
• [JS port] Add "prefix" argument to model loading method.
• Rename logging function from LOG to YDF_LOG to limit risk of collision with TF or Absl.

Fix

• [JS port] Fix memory leak. Release emscripten objects.

1.3.0

Features

• Setting the generic hyper-parameter "subsample" is enough enable random subsampling (to need to also set "sampling_method=RANDOM").
• Improve the display of decision tree structures.
• The Hyper-parameter optimizer field "predefined_search_space" automatically configures the set of hyper-parameters to explore during automatic hyper-parameter tuning.
• Replaces the MEAN_MIN_DEPTH variable importance with INV_MEAN_MIN_DEPTH.

1.2.0 - 2022-11-18

Features

• YDF can load TF-DF models directly (i.e. a TF model with a YDF model in the "assets" sub directory).
• Expose confusion tables in a GBT model's analysis.
• Add the "compute_variable_importances" tool to compute variable importances on an already trained model.
• Add the "experimental_analyze_model_and_dataset" tool to understand/analyze models.

Features

• Early stopping is no longer triggered during first iterations. The initial iteration for early stopping can be controlled with the new parameter early_stopping_initial_iteration in gradient_boosted_trees.proto.
• Benchmark inference tool does not require for the dataset to contain the label column.
• The user can specify the location of the wasm file in the JavaScript port.

Yggdrasil Decision Forests 1.0.0

With this release, Yggdrasil Decision Forests finally reaches its first major release 1.0.0 🥳

With this milestone we want to communicate more broadly that Yggdrasil Decision Forests has become a more stable and mature library. In particular, we established more comprehensive testing to make sure that YDF is ready for professional environments.

Features

• Go (GoLang) inference API (Beta): simple engine written in Go to do inference on YDF and TF-DF models.
• Creation of html evaluation report with plots (e.g., ROC, PR-ROC).
• Add support for Random Forest, CART, regressive GBT and Ranking GBT models in the Go API.
• Add customization of the number of IO threads in the deployment proto.

Fix

• Improved documentation.

Features

• Multi-threading of the oblique splitter for gradient boosted tree models.
• Support for Javascript + WebAssembly inference of model.
• Support for pure serving model i.e. model containing only serving data.
• Add "edit_model" cli tool.

Fix

• Remove bias toward low outcome in uplift modeling.

Pre-compiled binary for the Javascript + WebAssembly inference library.

Compiled with:

bazel build -c opt --config=lto --config=size --config=wasm //yggdrasil_decision_forests/port/javascript:create_release

Features

• Discard hessian splits with score lower than the parents. This change has little effect on the model quality, but it can reduce its size.
• Add internal flag hessian_split_score_subtract_parent to subtract the parent score in the computation of an hessian split score.
• Add the hyper-parameter optimizer as one of the meta-learner.
• The Random Forest and CART learners support the NUMERICAL_UPLIFT task.

Features

• Honest Random Forests (also work with Gradient Boosted Tree and CART).
• Can train Random Forests with example sampling without replacement.
• Add support for Focal Loss in Gradient Boosted Tree learner.

Fixes

• Incorrect default evaluation of categorical split with uplift tasks. This was making uplift models with missing categorical values perform worst, and made the inference of uplift model possibly slower.