葡萄酒品质 使用来自kaggle的数据集进行葡萄酒质量预测。

sklearn实现梯度下降(SGDRegressor)的Jupyter NoteBook文件

套索 概述 这是套索的实现。 拉索[ ] 套索的后裔坐标[J Friedman et al。， ; ] 最小角度回归（LARS）[Efron et al。， ] 有关算法的详细信息，请参见以下用日语编写的博客条目。 经过测试的环境 python == 3.8.3 numpy == 1.18.5 sklearn == 0.23.2

lpc matlab代码语音压缩最小二乘法 最小二乘音频和语音压缩线性预测编码（LPC） 这是设拉子大学线性代数课程的MATLAB项目，由Hamed Masnadi-Shirazi博士在2016年Spring学期授课。 项目指南也包括在内。

GBDT_Simple_Tutorial（梯度提升树简易教程） 简介 利用python实现GBDT算法的回归、二分类以及多分类，将算法流程详情进行展示解读并可视化，便于读者庖丁解牛地理解GBDT。 项目进度： 回归 二分类 多分类 可视化 算法原理以及公式推导请前往blog： 依赖环境 操作系统：Windows/Linux 编程语言：Python3 Python库：pandas、PIL、pydotplus， 其中pydotplus库会自动调用Graphviz，所以需要去下载graphviz的-2.38.msi ，先安装，再将安装目录下的bin添加到系统环境变量，此时如果再报错可以重启计算机。详细过程不再描述，网上很多解答。 文件结构 | - GBDT 主模块文件夹 | --- gbdt.py 梯度提升算法主框架 | --- decision_tree.py 单颗树生成，包括节点划分

PERMUTATION TESTS FOR JOINPOINT REGRESSION WITH APPLICATIONS TO CANCER RATES

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％。

逻辑回归 在全球范围内，心血管疾病（CVD）造成的死亡人数多于癌症。 从这项为期15年的心脏研究队列中收集的真实心脏病患者的数据集可用于此任务。 该数据集具有16个患者特征。 请注意，所有功能均不包含任何验血信息。 脚步 检查每个属性/列的描述性统计数据 检查班级不平衡 建立物流模型 确定特征重要性 评估模型的性能指标 要求 Python Google Colab 配套 将熊猫作为pd导入 从sklearn.datasets导入load_iris 从sklearn.linear_model导入LogisticRegression 从sklearn导入指标 从sklearn.metrics导入f1_score 导入matplotlib.pyplot作为plt 将numpy导入为np 从sklearn导入linear_model 从sklearn.model_selection导

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