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[DataMining] mackeyglass DL : 0: 产生两种makeglass数据，一定比例分割train、predict所言数据- generate make glass data
Date : 2025-12-16 Size : 2kb User : 陈翔
[DataMining] jueceshu DL : 0: 数据挖掘，决策树算法（对天气状况影响运不运动进行决策），代码完整，附带数据。-Data mining, decision tree algorithm (weather affect shipment don t make decisions), code complete, the supplementary data.
Date : 2025-12-16 Size : 12kb User : 赵阿阿
[DataMining] code_BPMF DL : 0: 如何使它工作： 1。创建一个单独的目录，并将所有这些文件下载到相同的目录中 2。下载7个文件： *demo:主文件demo:PMF和贝叶斯PMF * PMF.m:训练的PMF模型 * bayespmf.m贝叶斯PMF模型实现吉布斯采样器。 * moviedata.mat样本数据包含三元组（user_id，movie_id，评分） * makematrix.m：辅助功能转换成大型矩阵的三元组。 * PRED.m:辅助功能使得预测验证集。三.在Matlab只需运行演示。此代码使用MATLAB统计工具箱从威沙特分布的样本。-How to make it work: 1. Create a separate directory and download all these files into the same directory 2. Download the following 7 files: * demo.m Main file for training PMF and Bayesian PMF * pmf.m Training PMF model * bayespmf.m Bayesian PMF model that implements Gibbs sampler. * moviedata.mat Sample data that contains triplets (user_id, movie_id, rating) * makematrix.m Helper function that converts triplets into large matrix. This file is used by bayespmf.m * pred.m Helper function that makes predictions on the validation set. * README.txt 3. Simply run demo.m in Matlab. It will fit PMF and then will run Bayesian PMF. This code uses Matlab stats toolbox to sample Wishart distribution.
Date : 2025-12-16 Size : 4.45mb User : kobepudn
[DataMining] Boosting DL : 0: 分类，用于对数据进行归类。把数据按照不同的属性进行归类，并且使归类的精确度越高越好。(Classification; used to classify data. Classify data according to different attributes, and make the accuracy of classification better.)
Date : 2025-12-16 Size : 1kb User : goldsun18
[DataMining] EntropyBoost DL : 0: 用于对数据进行归类。把数据按照不同的属性进行归类，并且使归类的精确度越高越好。(used to classify data. Classify data according to different attributes, and make the accuracy of classification better.)
Date : 2025-12-16 Size : 5kb User : goldsun18
[DataMining] Python数据分析与挖掘实战 DL : 2: 本书共15章，分两个部分：基础篇、实战篇。基础篇介绍了数据挖掘的基本原理，实战篇介绍了一个个真实案例，通过对案例深入浅出的剖析，使读者在不知不觉中通过案例实践获得数据挖掘项目经验，同时快速领悟看似难懂的数据挖掘理论。读者在阅读过程中，应充分利用随书配套的案例建模数据，借助相关的数据挖掘建模工具，通过上机实验，以快速理解相关知识与理论。(There are 15 chapters in this book, which are divided into two parts: the basic chapter and the actual combat chapter. The basic chapter introduces the basic principles of data mining, and the actual combat section introduces a real case. Through the analysis of the cases, the readers can gain the experience of data mining projects through the case practice unconsciously, and quickly understand the seemingly difficult theory of data mining. In the process of reading, readers should make full use of the case modeling data provided with the book, with the help of relevant data mining modeling tools, and through computer experiments, to quickly understand the relevant knowledge and theory.)
Date : 2025-12-16 Size : 46.76mb User : zhaolong2020
[DataMining] python疫情数据可视化 DL : 0: 通过时事数据可视化系统，可以清楚地了解全球疫情分布的状况以及密度，以便做出相应的对策(Through the current affairs data visualization system, it is possible to clearly understand the distribution and density of the global epidemic in order to make corresponding countermeasures)
Date : 2025-12-16 Size : 384kb User : 一只小恼虎