[Roadmap] Multiple outputs.

Updates

In v3.4.0, the hist tree method is considered feature complete for the vector leaf.

Context

Since XGBoost 1.6, we have been working on having multi-output support for the tree model. In 2.0, we implemented the initial version of the vector-leaf-based multi-output model. This issue serves as a tracker for future development and related discussion. The original feature request is here: https://github.com/dmlc/xgboost/issues/2087 . The related features are for vector-leaf, not for general multi-output.

Feel free to share your suggestions or make related feature requests in the comments.

Implementation Optimization

• Use f-order for the gradient. Currently, the gradient has one column for each target but is written in C-order. The transformation takes about one-fifth of the training time. (#9508)
• Use f-order for the custom objective. (#9089)
• Improve array type dispatching by moving the dispatch logic from per-element to per-array. This enables us to have a more efficient custom objective interface. (#9090)

Algorithmic Optimization

We are still looking for potential algorithmic optimization for vector-leaf and here's the pool of candidates. We need to survey all available options. Feel free to share if you have ideas or paper recommendations.

• Sketch boost. (#11798, #11922)
• https://arxiv.org/abs/2201.06239 (#11798, #11922)
• Extra tree.

(#11798)

GPU Implementation

• Evaluation (#11781, #11883)
• Histogram (#11781, #11855)
• Prediction (#11752)
• Prediction cache. (#11862)
• Model (#11277)
• Partition. (#11789)
• Gradient sampling.

Documentation

• Derive the approximated Hessian in the context of boosting trees.

Multi-task

• Multi-task xgboost. This is not yet decided. I think it's wise to at least do some exploration before forging the rest of the implementation since we will have a very different interface if we need to consider multi-task. Related: https://github.com/dmlc/xgboost/issues/7693 .

Features

• Tree SHAP
• Plotting (#10093)
• Model text dump (JSON, txt, graphviz) (#10093, #11747)
• Tree data frame. (#12293)
• Categorical feature. (#12072, #12276, #12299, #12305)
• Interaction constraints (#12294)
• Monotonic constraints (#12341)
• Subsample.
• Column sampling.
• Approx tree method
• Exact tree method
• Loss weight
• Feature importance (be careful with tree index) (#10700)
• Intercept. (#11656)
• dart (#12340)

Learning to rank

We can have a ranking model to consider multiple criteria. This might require multi-task to be supported.

Quantile regression

• l1 https://github.com/dmlc/xgboost/pull/11917
• quantile https://github.com/dmlc/xgboost/pull/11917

Distributed

• Dask (#12292)
• PySpark
• Spark
• Flink?
• Federated (https://github.com/dmlc/xgboost/pull/9171)

Binding

• R (https://github.com/dmlc/xgboost/pull/9526)
• Scala
• Python
• Java
• C

HPO

• Check compatibility with major HPO frameworks.

Other extensions

• Sparse label. (multi-label classification optimization)
• Missing label.
• Early stopping for each target?

Applications

• https://arxiv.org/abs/2210.06831
• FIL

Benchmarks

• Collection of datasets for future comparison.