目录

Ensemble learning

misaraty 收录于学术

2022-03-09 约 443 字预计阅读 1 分钟

目录

前言

Ensemble learning algorithms: XGBoost(XGBoost.jl)、LightGBM(LightGBM.jl)、CatBoost(CatBoost.jl)、NGBoost、Random Forest等。

./ensemble_learning.png — Ensemble learning

./star-history-2023918.png — Github Star趋势

./Primary_ML_Software.jpg — Primary ML Software

Like last year, the most commonly used algorithms were linear and logistic regression, followed closely by decision trees and random forests. Of more complex methods, gradient boosting machines and convolutional neural networks were the most popular approaches.

./kaggle1.jpg — State of Data Science and Machine Learning 2021

We also saw strong year-over-year growth in the use of large language models such as transformer networks (BERT, GPT-3, etc).

./kaggle2.jpg — State of Data Science and Machine Learning 2021

Python-based tools continue to dominate the machine learning frameworks.

Like last year, Scikit-learn, a swiss army knife applicable to most projects, is the top with over 80% of data scientists using it. TensorFlow and Keras, notably used in combination for deep learning, were each selected on about half of the data scientist surveys. Gradient boosting library xgboost is fourth, with about the same usage as 2020 and 2019.

The most popular of the new tools added to the survey this year is Huggingface reaching over 10%.

./kaggle3.jpg — State of Data Science and Machine Learning 2021

Despite being used less frequently overall, we continue to see strong year-over-year growth of the PyTorch framework.

./kaggle4.jpg — State of Data Science and Machine Learning 2021

XGBoost

在线安装

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pip install xgboost

离线安装

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pip install xgboost-1.5.2-py3-none-manylinux2014_x86_64.whl

LightGBM

在线安装

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pip install lightgbm

离线安装

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pip install lightgbm-3.3.2-py3-none-manylinux1_x86_64.whl

CatBoost

在线安装

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pip install catboost

离线安装

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pip install graphviz-0.19.1-py3-none-any.whl
pip install catboost-1.0.4-cp38-none-manylinux1_x86_64.whl