logistic_regression 用logistic回归预测糖尿病数据集_我在糖尿病数据集上使用了logistic回归和决策树分类器模型，在对两个模型进行训练和测试数据集比率相同后，我发现logistic回归给出的准确性更高，大约为80％，而决策树分类器给出了约75％。

https://blog.csdn.net/ljw_study_in_CSDN/article/details/117775766 博客配套代码+数据集文件

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

适合机器学习初学者熟悉机器学习基本算法，以及数学建模比赛中直接对这些代码进行修改即可

机器学习编程作业逻辑回归.7z

logistic/sigmoid函数作用：把取值范围从负无穷到正无穷的公式计算结果，压缩到0和1之间，这样的输出值表达为“可能性”更直观。 逻辑回归算法用于估计预测目标的可能性，它属于软分类算法，即最终得到的是一个具体的概率，而不仅仅是“是”或“不是”这样的二分类结果；

Stroke_Prediction_6ML_models 该项目使用六个机器学习模型（XGBoost，随机森林分类器，支持向量机，逻辑回归，单决策树分类器和TabNet）进行笔画预测。 为此，我使用了Kaggle的“ healthcare-dataset-stroke-data”。 为了确定哪种模型最适合进行笔画预测，我绘制了每种模型的曲线下面积（AUC）。 AUC越高，模型越好

python逻辑回归案例源码

用逻辑回归制作评分卡时用到的数据。

对数几率回归（Logistic Regression），又称为逻辑回归的python实现，并且通过梯度下降法进行优化