Python based GBDT implementation

Overview

Py-boost: a research tool for exploring GBDTs

Modern gradient boosting toolkits are very complex and are written in low-level programming languages. As a result,

  • It is hard to customize them to suit one’s needs
  • New ideas and methods are not easy to implement
  • It is difficult to understand how they work

Py-boost is a Python-based gradient boosting library which aims at overcoming the aforementioned problems.

Authors: Anton Vakhrushev, Leonid Iosipoi.

Py-boost Key Features

Simple. Py-boost is a simplified gradient boosting library but it supports all main features and hyperparameters available in other implementations.

Fast with GPU. Despite the fact that Py-boost is written in Python, it works only on GPU and uses Python GPU libraries such as CuPy and Numba.

Easy to customize. Py-boost can be easily customized even if one is not familiar with GPU programming (just replace np with cp). What can be customized? Almost everuthing via custom callbacks. Examples: Row/Col sampling strategy, Training control, Losses/metrics, Multioutput handling strategy, Anything via custom callbacks

Installation

Before installing py-boost via pip you should have cupy installed. You can use:

pip install -U cupy-cuda110 py-boost

Note: replace with your cuda version! For the details see this guide

Quick tour

Py-boost is easy to use since it has similar to scikit-learn interface. For usage example please see:

More examples are comming soon

Other Sber AI Lab Projects

LightAutoML: https://github.com/sberbank-ai-lab/LightAutoML
AutoWoE: https://github.com/sberbank-ai-lab/AutoMLWhitebox
RePlay: https://github.com/sberbank-ai-lab/RePlay

Owner
Sberbank AI Lab
Sberbank AI Lab
A benchmark of data-centric tasks from across the machine learning lifecycle.

A benchmark of data-centric tasks from across the machine learning lifecycle.

61 Dec 28, 2022
A naive Bayes model for cancer classification using a set of documents

Naivebayes text classifcation model for cancer and noncancer documents Author: Alex King Purpose Requirements/files included How to use 1. Purpose The

Alex W King 1 Nov 24, 2021
mlpack: a scalable C++ machine learning library --

a fast, flexible machine learning library Home | Documentation | Doxygen | Community | Help | IRC Chat Download: current stable version (3.4.2) mlpack

mlpack 4.2k Jan 01, 2023
Project to deploy a machine learning model based on Titanic dataset from Kaggle

kaggle_titanic_deploy Project to deploy a machine learning model based on Titanic dataset from Kaggle In this project we used the Titanic dataset from

Vivian Yamassaki 8 May 23, 2022
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

AI Fairness 360 (AIF360) The AI Fairness 360 toolkit is an extensible open-source library containg techniques developed by the research community to h

1.9k Jan 06, 2023
A Python library for choreographing your machine learning research.

A Python library for choreographing your machine learning research.

AI2 270 Jan 06, 2023
Implementations of Machine Learning models, Regularizers, Optimizers and different Cost functions.

Linear Models Implementations of LinearRegression, LassoRegression and RidgeRegression with appropriate Regularizers and Optimizers. Linear Regression

Keivan Ipchi Hagh 1 Nov 22, 2021
A simple guide to MLOps through ZenML and its various integrations.

ZenBytes Join our Slack Community and become part of the ZenML family Give the main ZenML repo a GitHub star to show your love ZenBytes is a series of

ZenML 127 Dec 27, 2022
PyNNDescent is a Python nearest neighbor descent for approximate nearest neighbors.

PyNNDescent PyNNDescent is a Python nearest neighbor descent for approximate nearest neighbors. It provides a python implementation of Nearest Neighbo

Leland McInnes 699 Jan 09, 2023
Machine learning that just works, for effortless production applications

Machine learning that just works, for effortless production applications

Elisha Yadgaran 16 Sep 02, 2022
A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

pmdarima Pmdarima (originally pyramid-arima, for the anagram of 'py' + 'arima') is a statistical library designed to fill the void in Python's time se

alkaline-ml 1.3k Dec 22, 2022
SPCL 48 Dec 12, 2022
AutoX是一个高效的自动化机器学习工具,它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色、简单易用、通用、自动化、灵活。

English | 简体中文 AutoX是什么? AutoX一个高效的自动化机器学习工具,它主要针对于表格类型的数据挖掘竞赛。 它的特点包括: 效果出色: AutoX在多个kaggle数据集上,效果显著优于其他解决方案(见效果对比)。 简单易用: AutoX的接口和sklearn类似,方便上手使用。

4Paradigm 431 Dec 28, 2022
Fundamentals of Machine Learning

Fundamentals-of-Machine-Learning This repository introduces the basics of machine learning algorithms for preprocessing, regression and classification

Happy N. Monday 3 Feb 15, 2022
Breast-Cancer-Classification - Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms

Breast-Cancer-Classification - Using SKLearn breast cancer dataset which contains 569 examples and 32 features classifying has been made with 6 different algorithms

Mert Sezer Ardal 1 Jan 31, 2022
A library of sklearn compatible categorical variable encoders

Categorical Encoding Methods A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques

2.1k Jan 07, 2023
Distributed scikit-learn meta-estimators in PySpark

sk-dist: Distributed scikit-learn meta-estimators in PySpark What is it? sk-dist is a Python package for machine learning built on top of scikit-learn

Ibotta 282 Dec 09, 2022
Forecast dynamically at scale with this unique package. pip install scalecast

🌄 Scalecast: Dynamic Forecasting at Scale About This package uses a scaleable forecasting approach in Python with common scikit-learn and statsmodels

Michael Keith 158 Jan 03, 2023
A Collection of Conference & School Notes in Machine Learning 🦄📝🎉

Machine Learning Conference & Summer School Notes. 🦄📝🎉

558 Dec 28, 2022
We have a dataset of user performances. The project is to develop a machine learning model that will predict the salaries of baseball players.

Salary-Prediction-with-Machine-Learning 1. Business Problem Can a machine learning project be implemented to estimate the salaries of baseball players

Ayşe Nur Türkaslan 9 Oct 14, 2022