t-boost

t-boost is a machine learning algorithm that uses gradient boosting on oblivious decision trees. Its fitted model is exactly a set of rating tables: a Tabulating Boosting Machine (TBM). It is available as an open source library.

Gradient-boosted trees are usually more accurate than a GLM, but they are hard to read, review or deploy in systems built around rating tables. t-boost aims to get boosting-level accuracy in a model that is a set of main-effect and interaction tables, with no approximation and no surrogate model. It has a Rust core with Python bindings, takes polars DataFrames directly and is deterministic.

from t_boost import TBoostRegressor

model = TBoostRegressor(objective="poisson")
model.fit(train_data, "ClaimCount", exposure="Exposure")
tables = model.tables(train_data)   # the model, as rating tables

• Installation

  ---

  • Python package installation
  • Quick start
  • Build from source

• Training

  ---

  • Training
  • Training parameters
  • Using early stopping
  • Categorical features
  • Exposure and offsets
  • Panel data

• Rating tables

  ---

  • Rating tables
  • Prediction contributions
  • Feature importance
  • Actual versus expected

• Applying models

  ---

  • Regular prediction
  • Saving and loading models

• Metrics

  ---

  • Objectives and metrics
  • t_boost.metrics

• Educational materials

  ---

  • How training is performed
  • Pruning, banding and graduation
  • Parameter tuning

How it works

• Constrained trees. Each tree is symmetric (oblivious): every level applies one shared (feature, threshold) split. Each tree may use only a few distinct features, so the whole ensemble has a fixed maximum interaction order.
• Exact decomposition. Because of that structure, the trained ensemble can be rewritten as a functional-ANOVA (fANOVA) decomposition: one table per main effect and per interaction. The tables reproduce the model's predictions exactly, to floating-point tolerance.
• Purification. The tables are centred on the training data (exposure-weighted when an exposure is given), so each main effect carries as much of the signal as it can.
• The tables are the model. A saved model is stored as its rating tables, so what you review is exactly what gets deployed.

See How training is performed for details.