TreeBoost

Universal Tabular Learning Engine. Linear models, GBDTs, and Random Forests—unified.

At a Glance

Hybrid Linear+Tree learner that extrapolates trends while capturing interactions
AutoTuner and AutoML mode selection with conformal prediction built in
GPU acceleration (WebGPU, CUDA) plus AVX-512/SVE2 CPU backends
Zero-copy serialization and incremental TRB updates for production pipelines
Rust crate, CLI, and optional PyO3 bindings in one codebase

Quick Install

cargo add treeboost

# Optional Python bindings (requires Rust toolchain + maturin)
pip install treeboost

See Installation for feature flags and build notes.

Project Links

Docs: https://docs.rs/treeboost
Crate: https://crates.io/crates/treeboost
GitHub: https://github.com/ml-rust/treeboost

TreeBoost combines the extrapolation power of linear models, the interaction-capturing ability of gradient boosted trees, and the robustness of random forests—all in a single, zero-copy, production-ready Rust binary. GPU-accelerated out of the box.

Why TreeBoost?

Most tabular problems are solved by Linear, Tree, or their combination. Other libraries make you pick one. TreeBoost gives you all three through a single UniversalModel interface, plus automatic mode selection via the AutoTuner.

The Architecture:

┌─────────────────────────────────────────────────────────────┐
│                      UniversalModel                         │
├──────────────┬──────────────────────┬───────────────────────┤
│   PureTree   │   LinearThenTree     │    RandomForest       │
│   (GBDT)     │   (Hybrid)           │    (Bagging)          │
│              │                      │                       │
│ Best for:    │ Best for:            │ Best for:             │
│ - General    │ - Time-series        │ - Noisy data          │
│ - Categorics │ - Trending data      │ - Variance reduction  │
│              │ - Extrapolation      │ - Avoiding overfit    │
└──────────────┴──────────────────────┴───────────────────────┘

Why Rust?

Zero-copy, type-safe data handling
Deploy without Python runtime
Memory safety guarantees
Single binary, no dependencies

What You Get:

AutoML mode selection — instant data analysis picks PureTree, LinearThenTree, or RandomForest without expensive training trials.
Hybrid Linear+Tree architecture — LinearThenTree mode captures global trends with linear models, then trees learn the residuals. Extrapolates beyond training range.
Built-in preprocessing pipeline — Scalers, encoders, imputers that serialize with the model. No train/test skew.
Linear Trees — Decision trees with Ridge regression in leaves. 10-100x fewer trees for piecewise linear data.
Automatic hyperparameter tuning — AutoTuner with Latin Hypercube Sampling, k-fold CV, parallel evaluation. Tries all three modes automatically.
GPU acceleration — WGPU (all GPUs), CUDA (NVIDIA), with AVX-512/SVE2/scalar fallback
Production features — conformal prediction intervals, entropy regularization, ordered target encoding, zero-copy serialization

Automatic Hyperparameter Optimization

TreeBoost includes a production-ready AutoTuner that finds optimal hyperparameters automatically, eliminating manual tuning:

See examples/autotuner.rs for comprehensive examples.

AutoML Mode Selection

TreeBoost can analyze your dataset and pick the best boosting mode without a full training sweep.

use treeboost::{UniversalModel, MseLoss};

let model = UniversalModel::auto(&dataset, &MseLoss)?;
println!("Selected mode: {:?}", model.mode());
println!("Confidence: {:?}", model.selection_confidence());

This analysis uses fast linear/tree probes and produces a full report you can log or inspect.

Multi-Seed Ensemble Training

Combine predictions from multiple models trained with different random seeds:

use treeboost::{UniversalConfig, UniversalModel, BoostingMode, StackingStrategy};
use treeboost::loss::MseLoss;

// Train with 5 ensemble members, Ridge stacking
let config = UniversalConfig::new()
    .with_mode(BoostingMode::PureTree)
    .with_ensemble_seeds(vec![1, 2, 3, 4, 5])
    .with_stacking_strategy(StackingStrategy::Ridge {
        alpha: 0.01,
        rank_transform: false,
        fit_intercept: true,
        min_weight: 0.01,
    });

let model = UniversalModel::train(&dataset, config, &MseLoss)?;
let predictions = model.predict(&dataset);

Stacking strategies:

Ridge: Learns optimal weights via Ridge regression on out-of-fold predictions. Recommended for diverse ensembles.
Average: Simple equal-weight averaging. Fast and effective for homogeneous ensembles.

// Simple averaging
let config = UniversalConfig::new()
    .with_mode(BoostingMode::LinearThenTree)
    .with_ensemble_seeds(vec![42, 43, 44])
    .with_stacking_strategy(StackingStrategy::Average);

let model = UniversalModel::train(&dataset, config, &MseLoss)?;

Quick Start

Rust (Native)

use treeboost::{UniversalConfig, UniversalModel, BoostingMode};
use treeboost::dataset::DatasetLoader;
use treeboost::loss::MseLoss;

let loader = DatasetLoader::new(255);
let dataset = loader.load_parquet("data.parquet", "target", None)?;

// Choose your mode based on your data
let config = UniversalConfig::new()
    .with_mode(BoostingMode::LinearThenTree)  // Hybrid: linear trend + tree residuals
    .with_num_rounds(100)
    .with_linear_rounds(10)
    .with_learning_rate(0.1);

let model = UniversalModel::train(&dataset, config, &MseLoss)?;
let predictions = model.predict(&dataset);

Quick mode selection:

Your Data	Use This Mode
General tabular, categoricals	`BoostingMode::PureTree`
Time-series, trending, needs extrapolation	`BoostingMode::LinearThenTree`
Noisy data, want robustness	`BoostingMode::RandomForest`

Python (via PyO3)

import numpy as np
from treeboost import UniversalConfig, UniversalModel, BoostingMode

X = np.random.randn(10000, 20).astype(np.float32)
y = (X[:, 0] + X[:, 1] * 2 + np.random.randn(10000) * 0.1).astype(np.float32)

config = UniversalConfig()
config.mode = BoostingMode.LinearThenTree  # Hybrid mode
config.num_rounds = 100
config.linear_rounds = 10
config.learning_rate = 0.1

model = UniversalModel.train(X, y, config)
predictions = model.predict(X)

Architecture note: UniversalModel wraps GBDTModel internally—PureTree mode delegates directly to it. You get GPU acceleration, conformal prediction, and all mature features through either API. GBDTModel is still available for direct use if you prefer.

How It Works: Automatic Backend Selection

flowchart TD
    A{GPU Available?} -->|YES| B[WGPU Tensor-Tile<br/>Vulkan/Metal/DX12]
    A -->|NO| C{CPU Architecture}

    C -->|x86-64| D{AVX-512?}
    C -->|ARM| E{SVE2?}

    D -->|YES| F[AVX-512 Tensor-Tile<br/>vpconflictd parallel]
    D -->|NO| G[Scalar Backend<br/>AVX2 loads]

    E -->|YES| H[SVE2 Tensor-Tile<br/>HISTCNT direct]
    E -->|NO| I[Scalar Backend<br/>NEON loads]

WebGPU backend: Works on all GPUs (NVIDIA, AMD, Intel, Apple) via Vulkan, Metal, or DX12. Designed for portability - no installation required beyond your system drivers. Uses Hybrid mode (GPU histogram + CPU tree growth) due to WebGPU's higher dispatch overhead.

CUDA backend: Enables Full GPU mode with custom kernels - 2x+ faster than WebGPU on NVIDIA hardware. Low dispatch latency allows the entire tree building pipeline to run on GPU (histogram, partition, level-wise growth). The speedup grows with larger datasets. Optional but recommended for NVIDIA users.

Coming soon: Native Metal and ROCm backends for Apple and AMD GPUs.

CPU backends: AVX-512 (3rd Gen Xeon+), SVE2 (ARM Neoverse), with optimized scalar fallback.

Explicit Backend Selection

By default, TreeBoost auto-detects the best backend. Specify backends explicitly to override:

Rust:

use treeboost::{GBDTConfig, GBDTModel};
use treeboost::backend::BackendType;

let config = GBDTConfig::new()
    .with_num_rounds(100)
    .with_max_depth(6)
    .with_backend(BackendType::Scalar);  // Force CPU (AVX2/NEON)

let model = GBDTModel::train(&features, num_features, &targets, config, None)?;

Available backends:

BackendType::Scalar   // CPU: AVX2 (x86) or NEON (ARM) - no GPU overhead
BackendType::Avx512  // CPU: AVX-512 tensor-tile (x86-64 only)
BackendType::Sve2    // CPU: SVE2 tensor-tile (ARM only)
BackendType::Wgpu    // GPU: All GPUs via Vulkan/Metal/DX12 (portable)
BackendType::Cuda    // GPU: NVIDIA CUDA (2x+ faster than WGPU)
BackendType::Auto    // (Default) Auto-detect: CUDA > WGPU > AVX-512 > SVE2 > Scalar

Python:

from treeboost import GBDTConfig, GBDTModel

config = GBDTConfig()
config.num_rounds = 100
config.max_depth = 6
config.backend = "scalar"  # Force CPU

model = GBDTModel.train(X, y, config)

Performance

Competitive Benchmarks

Inference: Optimized for CPU execution via Rayon parallelism. Fast inference on standard compute eliminates GPU deployment overhead—no need for expensive GPU VMs just to serve predictions.

Training: Automatic backend selection balances speed and cost. CPU training is already fast for datasets <100K rows; GPU acceleration (CUDA/WGPU) provides significant speedup for larger datasets (100K–1B+ rows) where the computational advantage justifies GPU deployment.

Compared to other pure-Rust GBDT implementations:

Inference (per-batch prediction):

Dataset	TreeBoost	gbdt-rs	forust	Speedup
100 samples	47.4 µs	135.5 µs	92.9 µs	2.9x vs gbdt-rs
1K samples	202 µs	1.29 ms	893 µs	6.4x vs gbdt-rs
10K samples	539 µs	11.7 ms	8.9 ms	21.7x vs gbdt-rs

Training:

Dataset	TreeBoost	gbdt-rs	forust	Speedup
100K rows, 50 rounds	263 ms	3,389 ms	581 ms	12.9x vs gbdt-rs
100K rows, 100 rounds (parallel)	344 ms	6,600 ms	2,020 ms	19.2x vs gbdt-rs

Benchmarks: NVIDIA CUDA (Full GPU mode), raw float32 data, per-iteration time. See benches/competitors.rs for reproducible methodology.

Running Benchmarks:

# CPU-only comparison (fast, ~2 minutes)
cargo bench --bench competitors

# GPU-enabled comparison (with CUDA acceleration)
cargo bench --bench competitors --features gpu,cuda

# Python cross-library comparison
python benchmarks/benchmark.py --mode cross-library-gpu

Core Features

Robustness

Shannon Entropy regularization — Prevent drift across time windows
Pseudo-Huber loss — Automatic outlier handling (smoother than MSE)
Split Conformal Prediction — Distribution-free uncertainty intervals on predictions

Data Handling

Ordered Target Encoding — High-cardinality categoricals without target leakage
Count-Min Sketch — Automatic rare category compression (memory efficient)

Model Control

Monotonic/Interaction constraints — Enforce domain knowledge
Feature importance — Understand model decisions

Production

Zero-copy serialization — 100MB+ models load in milliseconds via rkyv
Streaming inference — Predict on 1M rows in seconds

Incremental Learning

TRB format — Custom journaled file format for incremental model updates
Warm-start training — Add trees to existing models without full retraining
O(1) appending — Updates append to file, no rewrite required
Crash recovery — CRC32 checksums detect corruption, partial writes recovered
Drift detection — Monitor distribution shifts between training batches

The Hybrid Architecture

How LinearThenTree Works

The LinearThenTree mode implements what's sometimes called "Residual Boosting" or "Linear-Forest":

Final Prediction = Linear(x) + Trees(x)
                   ↑              ↑
                   │              └── Captures non-linear patterns, interactions
                   └── Captures global trend (can extrapolate!)

Phase 1: Train a Ridge/LASSO/ElasticNet model on all features
Phase 2: Compute residuals: r = y - linear_prediction
Phase 3: Train GBDT on residuals (the stuff linear couldn't explain)

This is powerful for data with underlying trends (time-series, pricing, growth curves). Pure trees can't extrapolate—they're bounded by training data. The linear component can.

LinearTreeBooster (Different Thing!)

Don't confuse LinearThenTree mode with LinearTreeBooster. They solve different problems:

	LinearThenTree (Mode)	LinearTreeBooster (Learner)
Structure	1 global linear + many standard trees	Trees with linear models in each leaf
Best for	Global trends + local non-linearities	Piecewise linear data (tax brackets, physics)
Trees needed	Normal (50-200)	Very few (5-20)

Use LinearTreeBooster when your data looks like segments with different slopes—the tree finds the breakpoints, Ridge fits each segment.

Preprocessing That Travels With Your Model

TreeBoost's preprocessing pipeline serializes with your model:

use treeboost::preprocessing::{PipelineBuilder, StandardScaler, SimpleImputer};

let pipeline = PipelineBuilder::new()
    .add_standard_scaler(&["price", "quantity"])
    .add_simple_imputer(&["category"], ImputeStrategy::Mode)
    .add_frequency_encoder(&["category"])
    .build();

// Fit on training data
pipeline.fit(&train_df)?;

// Transform both train and test identically
let train_transformed = pipeline.transform(&train_df)?;
let test_transformed = pipeline.transform(&test_df)?;

// Pipeline state saved with model - no train/test skew at inference

For Trees: Use FrequencyEncoder or LabelEncoder. OneHot creates sparse nightmares.

For Linear models: Use StandardScaler (essential!) and OneHotEncoder (linear needs binary indicators).

For Hybrid (LinearThenTree): The linear component gets internally standardized. You can still preprocess for the tree component.

Incremental Learning

TreeBoost supports incremental model updates via the TRB (TreeBoost) file format—a custom journaled format optimized for appending without rewriting the base model.

Why Incremental Learning?

Avoid full retraining — Add trees to existing models with new data
Real-time adaptation — Update models daily/hourly as data arrives
Lower compute costs — Train on new data only, not entire history

Rust:

use treeboost::{AutoModel, UniversalModel};
use treeboost::dataset::DatasetLoader;
use treeboost::loss::MseLoss;

// 1. Initial training via AutoModel (convenience wrapper)
let auto = AutoModel::train(&df_january, "target")?;

// 2. Save UniversalModel to TRB format
auto.inner().save_trb("model.trb", "Initial training on January data")?;

// 3. Later: Load and update with new data (uses UniversalModel directly)
let mut model = UniversalModel::load_trb("model.trb")?;
let loader = DatasetLoader::new(255);
let new_dataset = loader.load_parquet("february.parquet", "target", None)?;
let report = model.update(&new_dataset, &MseLoss, 10)?;  // Add 10 trees
println!("Trees: {} -> {}", report.trees_before, report.trees_after);

// 4. Append update to same file (O(1) append, no rewrite)
model.save_trb_update("model.trb", new_dataset.num_rows(), "February update")?;

// 5. Inference: Load and predict with BinnedDataset
let model = UniversalModel::load_trb("model.trb")?;
let predictions = model.predict(&new_dataset);

Note: TRB format stores UniversalModel only. Use AutoModel for initial training convenience, then work with UniversalModel + BinnedDataset for incremental updates and inference.

The TRB Format:

┌──────────────────────────────────────────────────────────┐
│ Header (magic, version, model type, created_at, ...)     │
├──────────────────────────────────────────────────────────┤
│ Base Model Blob + CRC32                                  │
├──────────────────────────────────────────────────────────┤
│ Update 1: Header + Blob + CRC32  (appended)              │
├──────────────────────────────────────────────────────────┤
│ Update 2: Header + Blob + CRC32  (appended)              │
└──────────────────────────────────────────────────────────┘

Journaled appends — Updates append to file end, base model untouched
CRC32 per segment — Detect corruption at segment level
Crash recovery — Truncated writes detected and skipped on load
Forward compatible — Unknown JSON fields in headers ignored

Drift Detection:

Monitor distribution shifts between training batches:

use treeboost::monitoring::{IncrementalDriftDetector, check_drift};

// Create detector from training data
let detector = IncrementalDriftDetector::from_dataset(&train_data);

// Before updating, check for drift
let result = detector.check_update(&new_data);
if result.has_significant_drift() {
    println!("Warning: {}", result);
    println!("Recommendation: {}", result.recommendation);
    // Consider full retrain instead of incremental update
}

Installation

Rust Library

cargo add treeboost

Python Package

# From PyPI
pip install treeboost

# From source (requires Rust toolchain)
git clone https://github.com/your-org/treeboost
cd treeboost
pip install maturin && maturin develop --release

Feature Flags

Feature	Description	Use Case
`gpu`	WGPU backend (Vulkan/Metal/DX12)	All GPUs, portable
`cuda`	NVIDIA CUDA backend	2x+ faster than WGPU on NVIDIA
`mmap`	Memory-mapped TRB loading	Instant model load, zero-copy I/O
`python`	PyO3 bindings	Python interop

Enable features:

# GPU acceleration
cargo build --release --features gpu

# CUDA (NVIDIA only, requires CUDA 12.x)
cargo build --release --features cuda

# Memory-mapped model loading (instant load for large models)
cargo build --release --features mmap

Memory-mapped loading (mmap feature):

For large models (100MB+), mmap provides true zero-copy I/O:

#[cfg(feature = "mmap")]
{
    use treeboost::serialize::MmapTrbReader;

    // Instant load - OS pages data lazily, no heap allocation
    let reader = MmapTrbReader::open("model.trb")?;
    let model = reader.load_model()?;  // Still faster than TrbReader
}

Reader	Load Time	Memory	Use Case
`TrbReader`	O(model_size)	O(model_size)	Default, works everywhere
`MmapTrbReader`	O(1)	O(1) initial	Large models, inference servers

More Examples

Rust: Train, Save Config, and Save Model

use treeboost::{AutoModel, UniversalModel};

// Train with AutoML (discovers best mode and hyperparameters)
let auto = AutoModel::train(&df, "target")?;

// Save the discovered configuration to JSON (useful for inspection and reuse)
auto.save_config("best_config.json")?;

// Save the trained model for inference
auto.save("model.rkyv")?;

// Later: Load and predict (no need to retrain)
let loaded = UniversalModel::load("model.rkyv")?;
let predictions = loaded.predict(&dataset);
let importances = loaded.feature_importance();

Export config to inspect discovered hyperparameters:

// After training with AutoML
let auto = AutoModel::train(&df, "target")?;

// Export to JSON
let config_json = serde_json::to_string_pretty(auto.config())?;
std::fs::write("config.json", config_json)?;

// Inspect the JSON to see what mode was chosen,
// learning rates, ensemble seeds, etc.
// Then manually adjust and retrain if needed

Python: Conformal Prediction

from treeboost import GBDTConfig, GBDTModel

X = np.random.randn(10000, 50).astype(np.float32)
y = np.sum(X[:, :5], axis=1) + np.random.randn(10000) * 0.5

config = GBDTConfig()
config.num_rounds = 100
config.max_depth = 6
config.calibration_ratio = 0.2    # Reserve 20% for uncertainty estimation
config.conformal_quantile = 0.9   # 90% prediction intervals

model = GBDTModel.train(X, y, config)
preds, lower, upper = model.predict_with_intervals(X_test)

# Now you have uncertainty bounds on every prediction
print(f"Prediction: {preds[0]:.2f}, [{lower[0]:.2f}, {upper[0]:.2f}]")

Python: Categorical Features

import pandas as pd
from treeboost import GBDTConfig, GBDTModel

df = pd.read_csv("data.csv")

# Target encoding for high-cardinality categorical
config = GBDTConfig()
config.num_rounds = 100
config.use_target_encoding = True    # Ordered encoding, no leakage
config.cms_threshold = 100           # Rare categories → "Unknown"

X = df[feature_cols].values.astype(np.float32)
y = df['target'].values.astype(np.float32)

model = GBDTModel.train(X, y, config)

Automatic Hyperparameter Tuning

Rust:

use treeboost::{AutoTuner, TunerConfig, GridStrategy, EvalStrategy, ParameterSpace, SpacePreset};

let tuner_config = TunerConfig::new()
    .with_iterations(3)
    .with_grid_strategy(GridStrategy::LatinHypercube { n_samples: 50 })
    .with_eval_strategy(EvalStrategy::holdout(0.2).with_folds(5)) // 5-fold CV
    .with_verbose(true);

let mut tuner = AutoTuner::new(GBDTConfig::new())
    .with_config(tuner_config)
    .with_space(ParameterSpace::with_preset(SpacePreset::Regression))
    .with_callback(|trial, current, total| {
        println!("Trial {}/{}: val_loss={:.4}", current, total, trial.val_metric);
    });

let (best_config, history) = tuner.tune(&dataset)?;
println!("Best validation loss: {:.6}", history.best().unwrap().val_metric);

// Train final model with best configuration
let final_model = GBDTModel::train_binned(&dataset, best_config)?;

Python:

from treeboost import AutoTuner, TunerConfig, GridStrategy, EvalStrategy, ParameterSpace

tuner = AutoTuner(GBDTConfig())
tuner_config = (
    TunerConfig.preset("thorough")
    .with_grid_strategy(GridStrategy.lhs(50))
    .with_eval_strategy(EvalStrategy.holdout(0.2).with_folds(5))
    .with_verbose(True)
)
tuner.config = tuner_config
tuner.space = ParameterSpace.preset("regression")

best_config, history = tuner.tune(X, y)
print(f"Best validation loss: {history.best().val_metric:.6f}")

# Train final model
model = GBDTModel.train(X, y, best_config)

CLI Tool

If you're using the binary distribution:

# Train a model (rkyv format for static models)
treeboost train --data data.csv --target price --output model.rkyv \
  --rounds 100 --max-depth 6 --learning-rate 0.1

# Make predictions
treeboost predict --model model.rkyv --data test.csv --output predictions.json

# Inspect the model
treeboost info --model model.rkyv --importances

# Incremental updates (TRB format)
treeboost update --model model.trb --data new_data.csv --target price --rounds 10

Incremental Learning via CLI:

# Inspect a TRB file (shows update history)
treeboost info --model model.trb
# Output:
#   Format version: 1
#   Created: 2024-01-15 10:30:00 UTC
#   Update History:
#     Update 1: 2024-02-01 09:00:00 UTC (500 rows, "February data")
#     Update 2: 2024-03-01 09:00:00 UTC (450 rows, "March data")
#   Current tree count: 120

# Update with new data
treeboost update --model model.trb --data april.csv --target price \
  --rounds 10 --description "April update"

# Force load despite corrupted updates (loads base only)
treeboost info --model model.trb --force

Run treeboost <command> --help for all available options.

Configuration Reference

Core Hyperparameters

Parameter	Default	Description
`num_rounds`	100	Number of boosting iterations
`max_depth`	6	Maximum tree depth (deeper = more expressive but slower)
`learning_rate`	0.1	Shrinkage per round (lower = more stable but slower training)
`max_leaves`	31	Maximum leaves per tree
`lambda`	1.0	L2 leaf regularization
`loss`	`mse`	`mse` or `huber` (huber for outliers)

Advanced Features

Parameter	Default	Description
`entropy_weight`	0.0	Shannon entropy penalty (prevents drift)
`subsample`	1.0	Row sampling ratio per round
`colsample`	1.0	Feature sampling ratio per tree
`calibration_ratio`	0.0	Fraction of data reserved for conformal calibration
`conformal_quantile`	0.9	Quantile for prediction intervals (0.9 = 90% coverage)
`use_target_encoding`	false	Enable ordered target encoding for categoricals
`cms_threshold`	0	Rare category threshold (0 = disabled)

Constraints

config.monotonic_constraints = [
    MonotonicConstraint.Increasing,   # Feature 0
    MonotonicConstraint.None,         # Feature 1
    MonotonicConstraint.Decreasing,   # Feature 2
]

config.interaction_groups = [
    [0, 1, 2],  # These features can interact
    [3, 4],     # Separate interaction group
]

Troubleshooting

Check which backend is being used:

RUST_LOG=treeboost=debug treeboost train ...

GPU not detected:

Verify your GPU drivers are installed (NVIDIA, AMD, Intel, or Apple)
WGPU supports Vulkan (Linux), Metal (macOS), DX12 (Windows)
For NVIDIA CUDA: Install CUDA 12.x separately

Out of memory during training:

treeboost train ... --subsample 0.8 --colsample 0.8

Model won't load:

Ensure you're using the same TreeBoost version for save/load
The .rkyv file is tied to the binary layout; recompiling TreeBoost may break compatibility

Acknowledgments

TreeBoost builds on the collective knowledge of the GBDT community. We acknowledge the following projects that shaped our design and implementation:

XGBoost — Industry-standard GBDT with GPU support; inspired our histogram-based approach and Full GPU mode architecture.
LightGBM — Leaf-wise growth strategy and histogram optimization techniques.
CatBoost — Ordered target encoding for categorical features and conformal prediction intervals.
Forust — Pure-Rust GBDT implementation; motivated our focus on Rust-first performance.
WarpGBM — GPU-accelerated histogram building patterns.

License

Apache License 2.0

treeboost 0.1.0

TreeBoost

At a Glance

Quick Install

Project Links

Why TreeBoost?

Automatic Hyperparameter Optimization

AutoML Mode Selection

Quick Start

Rust (Native)

Python (via PyO3)

How It Works: Automatic Backend Selection

Explicit Backend Selection

Performance

Competitive Benchmarks

Core Features

Robustness

Data Handling

Model Control

Production

Incremental Learning

The Hybrid Architecture

How LinearThenTree Works

LinearTreeBooster (Different Thing!)

Preprocessing That Travels With Your Model

Incremental Learning

Installation

Rust Library

Python Package

Feature Flags

More Examples

Rust: Train, Save Config, and Save Model

Python: Conformal Prediction

Python: Categorical Features

Automatic Hyperparameter Tuning

CLI Tool

Configuration Reference

Core Hyperparameters

Advanced Features

Constraints

Troubleshooting

Acknowledgments

License