如何在WEKA中交叉validation后打印预测类
使用分类器完成10倍交叉validation后，如何打印出每个实例的预测类以及这些实例的分布？
J48 j48 = new J48(); Evaluation eval = new Evaluation(newData); eval.crossValidateModel(j48, newData, 10, new Random(1)); 当我尝试类似于下面的内容时，它说分类器没有构建 。
for (int i=0; i<data.numInstances(); i++){ System.out.println(j48.distributionForInstance(newData.instance(i))); }
我正在尝试做的是与WEKA GUI中相同的function，其中一旦训练了分类器，我就可以点击Visualize classifier error" > Save ，我将在文件中找到预测的类。但是现在我需要它在我自己的Java代码中工作。
我尝试过类似下面的内容：
J48 j48 = new J48(); Evaluation eval = new Evaluation(newData); StringBuffer forPredictionsPrinting = new StringBuffer(); weka.core.Range attsToOutput = null; Boolean outputDistribution = new Boolean(true); eval.crossValidateModel(j48, newData, 10, new Random(1), forPredictionsPrinting, attsToOutput, outputDistribution);
然而它提示我错误：
Exception in thread "main" java.lang.ClassCastException: java.lang.StringBuffer cannot be cast to weka.classifiers.evaluation.output.prediction.AbstractOutput
crossValidateModel()方法可以使用forPredictionsPrinting varargs参数，该参数是weka.classifiers.evaluation.output.prediction.AbstractOutput实例。
其中重要的部分是StringBuffer来保存所有预测的字符串表示。 以下代码在未经测试的JRuby ，但您应该能够根据需要进行转换。
j48 = j48.new eval = Evalution.new(newData) predictions = java.lange.StringBuffer.new eval.crossValidateModel(j48, newData, 10, Random.new(1), predictions, Range.new('1'), true) # variable predictions now hold a string of all the individual predictions
几天前我被卡住了。 我想用matlab评估matlab中的Weka分类器，而不是从arff文件加载。 我使用http://www.mathworks.com/matlabcentral/fileexchange/21204-matlab-weka-interface和以下源代码。 我希望这可以帮助别人。
import weka.classifiers.*; import java.util.* wekaClassifier = javaObject('weka.classifiers.trees.J48'); wekaClassifier.buildClassifier(processed);%Loaded from loadARFF e = javaObject('weka.classifiers.Evaluation',processed);%Loaded from loadARFF myrand = Random(1); plainText = javaObject('weka.classifiers.evaluation.output.prediction.PlainText'); buffer = javaObject('java.lang.StringBuffer'); plainText.setBuffer(buffer) bool = javaObject('java.lang.Boolean',true); range = javaObject('weka.core.Range','1'); array = javaArray('java.lang.Object',3); array(1) = plainText; array(2) = range; array(3) = bool; e.crossValidateModel(wekaClassifier,testing,10,myrand,array) e.toClassDetailsString
AsdrúbalLópez-Chau
clc clear %Load from disk fileDataset = 'cm1.arff'; myPath = 'C:\Users\Asdrubal\Google Drive\Respaldo\DoctoradoALCPC\Doctorado ALC PC\AlcMobile\AvTh\MyPapers\Papers2014\UnderOverSampling\data\Skewed\datasetsKeel\'; javaaddpath('C:\Users\Asdrubal\Google Drive\Respaldo\DoctoradoALCPC\Doctorado ALC PC\AlcMobile\JarsForExperiments\weka.jar'); wekaOBJ = loadARFF([myPath fileDataset]); %Transform from data into Matlab [data, featureNames, targetNDX, stringVals, relationName] = ... weka2matlab(wekaOBJ,'[]'); %Create testing and training sets in matlab format (this can be improved) [tam, dim] = size(data); idx = randperm(tam); testIdx = idx(1 : tam*0.3); trainIdx = idx(tam*0.3 + 1:end); trainSet = data(trainIdx,:); testSet = data(testIdx,:); %Trasnform the training and the testing sets into the Weka format testingWeka = matlab2weka('testing', featureNames, testSet); trainingWeka = matlab2weka('training', featureNames, trainSet); %Now evaluate classifier import weka.classifiers.*; import java.util.* wekaClassifier = javaObject('weka.classifiers.trees.J48'); wekaClassifier.buildClassifier(trainingWeka); e = javaObject('weka.classifiers.Evaluation',trainingWeka); myrand = Random(1); plainText = javaObject('weka.classifiers.evaluation.output.prediction.PlainText'); buffer = javaObject('java.lang.StringBuffer'); plainText.setBuffer(buffer) bool = javaObject('java.lang.Boolean',true); range = javaObject('weka.core.Range','1'); array = javaArray('java.lang.Object',3); array(1) = plainText; array(2) = range; array(3) = bool; e.crossValidateModel(wekaClassifier,testingWeka,10,myrand,array)%U e.toClassDetailsString