weka datasets

From: http://www.shen-wei.cn/shenwei/Class.asp? Id = 30 · WEKA Learning Experience Summary 3 (WEKA development-import WEKA package)WEKA Learning Experience Summary 3 (WEKA development-import WEKA package)... [view full text] ·

Rweka(http://cran.r-project.org/web/packages/RWeka/index.html):1) data input and outputWOW (): View the parameters of the Weka function.Weka_control (): Sets the parameters of the Weka function.Read.arff (): reads the data weka attribute-relation File format (ARFF).Write.arff: Writes data to Weka attribute-relation fil

Several basic concepts and two basic algorithms for association rules are described in the previous few. But actually in the commercial application, the writing algorithm is less than, understands the data, grasps the data, uses the tool to be important, the preceding basic article is to the algorithm understanding, this article will introduce the open source utilizes the data Mining tool Weka to carry on the management rule mining.

(EXT) http://baidutech.blog.51cto.com/4114344/1033714/1. IntroductionData mining, machine learning these words, in some people's view, is the threshold of very high things. Admittedly, it does require a lot of background knowledge to do algorithm implementation and even algorithm optimization. But the fact is, the vast majority of data mining engineers do not need to do the algorithm level of things. Their energies are concentrated on feature extraction, algorithm selection and parameter tuning.

Sender: finallyliuyu (lead donkey), email area: NLP Question: solutions to weka jvm outofmemory Problems Mail station: Shui mu Community (Sun Feb 20 10:36:12 2011), Station We are using WEKA for text classification experiments (mainly focusing on measuring Feature Word selection)Algorithm).Corpus: reteurs21578Featurewords 100Training set: 6950Test Set: 2676(In sparse data storage mode) ARFF file size:

, and finally calculates the classification Input: mnist DataSet or Fashion mnist dataset Output: Error rate and accuracy Mnist Data set: Take k=30, the verification set is 50, the accuracy rate is 1; Take k=30, the verification set is 500, the accuracy rate is 0.98; Take k=30, the validation set is 10,000, the accuracy rate is 0.84. Fashion mnist Data Set K=30, when the validation set is 10000, the t

; // the smallest information gain rate double currentresult; // The current information gain rate c45split [] currentmodel; // stores all models generated by unclassified attributes c45split bestmodel = NULL; // currently, the best model nosplit nosplitmodel = NULL; // represents the Model Double averageinfogain = 0; // The average information gain of each model (currentmodel) int validmodels = 0; // valid model Boolean multival = true; // multi-value distribution checkdistribution; // training

Source code analysis of Weka algorithm Classifier-tree-J48 (3) ModelSelection ModelSelection is mainly used to select the appropriate columns to split the dataset. Combined with the main process of J48 in the previous article, we found that ModelSelection is C45ModelSelection and BinC45ModelSelection, so we can analyze C45ModelSelection first. I. C45ModelSelection As a ModelSelection interface, there are two main implementation methods: selectModel

1. My environment Operating system: 32-bit Win7 flagship Service Pack 1 2. Download Weka Weka page address on OSC: Http://www.oschina.net/p/weka Software Home Address: http://www.cs.waikato.ac.nz/ml/weka/ Each version of Weka runs the required Java version, which can be foun

Note: 1. This document assumes that you have installed JDK and obtained WEKA installation. Program .2, on WEKA source code: installation directory has a weka-src.jar unzip is the source code.3. I don't know much about the details of the jcreator, but I barely ran WEKA. If you have any suggestions, please kindly advise.

Configuring Weka in Eclipse　1 Find Weka Installation location, look for Weka compressed file Weka-src.jar, unzip the compressed file, extract the folder Weka-src.2 Open Eclipse, New Java Project Project Weka.3 Select Package src,

Weka is a very useful machine learning library, which is not described in detail here. To get to the point, to use Weka in a program way, here are the steps: First, create a new Java project in eclipse: 1. Create the project: Click File->new->java Project in the menu, and any name in project name in the pop-up dialog box is assumed to be wekatest. Click the Finish button (at the bottom of the dialog box). 2