gplearn：Python中的遗传编程，具有受scikit-learn启发的API

一本很好的关于逻辑回归分析的书 Review "...The book is a classic, extremely well written, and it includes a variety of software packages and real examples...." -- The Statistician, Vol. 51, No.2, 2002 "...an excellent book that balances many objectives well.... All statistical practitioners...can benefit from this book...Applied Logistic Regression is an ideal choice." -- Technometrics, February 2002 "...it remains an extremely valuable text for everyone working or teaching in fields like epidemiology..." -- Statistics in Medicine, No.21, 2002 "...the revised text continues to provide a focused introduction to the logistic regression model and its use in methods for modelling..." -- Short Book Reviews, Vol. 21, No. 2, August 2001 "In this revised and updated edition of the popular test, the authors incorporate theoretical and computing advances from the last decade." -- Journal of the American Statistical Association, September 2001 Product Description From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references."-Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent."-Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical."-The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic reg

Google Earth engine(GEE )实现随机森林的回归算法.

GroongaPackages回归测试 Groonga 包的测试工具。 要求 安装 $ gem install groonga_packages_regression_test 用法 $ groonga_packages_regression_test pull $ groonga_packages_regression_test install $ groonga_packages_regression_test go 作者 横山文 执照 LGPLv2.1 或更高版本。 有关详细信息，请参阅。

回归森林的matlab代码。转载，版权abhirana所有。

预算matlab代码###线性回归模板 #### Description在MATLAB和python中实现的线性回归算法。 使用梯度下降或正态方程学习回归参数。 允许任何（合理）数量的连续特征。 将输入（csv文件）分为训练集和测试集。 使用训练集学习参数，并计算训练集和测试集上的误差。 如果使用梯度下降（脚本中的set选项），则绘制成本函数收敛图。 基于的Ex.1的代码。 ####用于开发和测试的数据集 ####文件MATLAB回归脚本：linear_regression_script.m 脚本的功能形式（允许在函数调用中指定参数）：linear_regression.m 回归脚本的Python实现：lin_reg.py 主脚本和函数使用的MATLAB函数：computeCostMulti.m，gradientDescentMulti.m

基于Logistic Regression模型实现手写数字识别

DBN-for-regression-master源码.rar

作者：Joseph Michael Hilbe，Wiki简介：https://en.wikipedia.org/wiki/Joseph_Hilbe 负二项回归属于广义线性回归（GLM）的分支，与Logistic回归、Poisson回归等都属于计数数据模型的范畴，主要用于以分类变量、定序变量为因变量的回归分析之中。 负二项回归家族庞大，逐渐应用于社会科学领域各个学科的统计分析建模之中，本书详细介绍了负二项回归分析的原理以及该模型的多种变体，为该方法的学习提供了重要指导。

Instabilities of Regression Estimates Relating Air Pollution to Mortality