Perpetual

A self-generalizing, hyperparameter-free gradient boosting machine

PerpetualBooster is a gradient boosting machine (GBM) algorithm which doesn't have hyperparameters to be tuned so that you can use it without needing hyperparameter optimization packages unlike other GBM algorithms. Similar to AutoML libraries, it has a budget parameter which ranges between (0, 1). Increasing the budget parameter increases predictive power of the algorithm and gives better results on unseen data. Start with a small budget and increase it once you are confident with your features. If you don't see any improvement with further increasing budget, it means that you are already extracting the most predictive power out of your data.

Benchmark

Hyperparameter optimization usually takes 100 iterations with plain GBM algorithms. PerpetualBooster achieves the same accuracy in the single run. Thus, it achieves around 100x speed-up at the same accuracy with different budget levels and with different datasets. The speed-up might be slightly lower or significantly higher than 100x depending on the dataset.

The following table summarizes the results for the California Housing dataset:

You can reproduce the results using the performance_benchmark.ipynb notebook in the examples folder.

Usage

You can use the algorithm like in the example below. Check examples folders for both Rust and Python.

from perpetual import PerpetualBooster

model = PerpetualBooster(objective="SquaredLoss")
model.fit(X, y, budget=0.4)

Documentation

Documentation for the Python API can be found here and for the Rust API here.

Installation

The package can be installed directly from pypi.

pip install perpetual

To use in a rust project, add the following to your Cargo.toml file to get the package from crates.io.

perpetual = "0.1.0"

Paper

PerpetualBooster prevents overfitting with a generalization algorithm. The paper is work-in-progress to explain how the algorithm works.