weka

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: If I use Weka Explorer to run some training data against testing data using SVM with a linear kernel, everything is fine. But I need to do this programmatically in my own Java and my current code looks like this: Instances train = new Instances (...); train . setClassIndex ( train . numAttributes () - 1 ); Instances test = new Instances (...) + ClassificationType classificationType = ClassificationTypeDAO . get ( 6 ); LibSVM libsvm = new LibSVM (); String options = ( classificationType . getParameters ()); String [] optionsArray =

I am using GUI version of WEKA and I am classifying using the Naive Bayes. Can anyone please let me know how to find out which instances are misclassified. Go to classify tab in Weka explorer Click more options... Check output predictions Click OK Hope that helps. I faced this very same problem earlier and I tackle it just fine now. What I do, is the following: Make one String attribute that assigns each instance a unique ID. I have assigned the names of the documents to each of my instances. Generate the WEKA supported .arff file. Whenever you have to run a classifier on this .arff data, you

I would like to use an Attribute-Relation File Format with scikit-learn to do some NLP task, is this possible? How can use an .arff file with scikit-learn ? I really recommend liac-arff . It doesn't load directly to numpy, but the conversion is simple: import arff, numpy as np dataset = arff.load(open('mydataset.arff', 'rb')) data = np.array(dataset['data']) I found that scipy has a loader for arff files to load them as numpy record arrays. I am not 100% sure that those arrays are suitable for direct consumption by scikit-learn but that should get your started. Follow renatopp's answer: assume

I've created a dataset which contains +/- 13000 rows with +/- 50 features. I know how to output every classification result: prediction and actual, but I would like to be able to output some sort of ID with those results. So i've added a ID column to my dataset but I don't know how disregard the ID when classifying while still being able to output the ID with every prediction result. I do know how to select features to output with every prediction. Use FilteredClassifier. See this and this . Let's say follwoing are the attributes in the bbcsport.arff that you want to remove and is in a file

When using Kmeans in Weka, one can call getAssignments() on the resulting output of the model to get the cluster assignment for each given instance. Here's a (truncated) Jython example: >>>import weka.clusterers.SimpleKMeans as kmeans >>>kmeans.buildClusterer(data) >>>assignments = kmeans.getAssignments() >>>assignments >>>array('i',[14, 16, 0, 0, 0, 0, 16,...]) The index of each cluster number corresponds to the instance. So, instance 0 is in cluster 14, instance 1 is in cluster 16, and so on. My question is: Is there something similar for Xmeans? I've gone through the entire API here and don

Please help interpret results of logistic regression produced by weka.classifiers.functions.Logistic from Weka library. I use numeric data from Weka examples: @relation weather @attribute outlook {sunny, overcast, rainy} @attribute temperature real @attribute humidity real @attribute windy {TRUE, FALSE} @attribute play {yes, no} @data sunny,85,85,FALSE,no sunny,80,90,TRUE,no overcast,83,86,FALSE,yes rainy,70,96,FALSE,yes rainy,68,80,FALSE,yes rainy,65,70,TRUE,no overcast,64,65,TRUE,yes sunny,72,95,FALSE,no sunny,69,70,FALSE,yes rainy,75,80,FALSE,yes sunny,75,70,TRUE,yes overcast,72,90,TRUE,yes

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I have a few arff files. I would like to read them sequentially and create a large dataset. Instances.add(Instance inst) doesn't add string values to the instances, hence the attempt to setDataset() ... but even this fails. Is there a way to accomplish the intuitively correct thing for strings? ArffLoader arffLoader = new ArffLoader(); arffLoader.setFile(new File(fName)); Instances newData = arffLoader.getDataSet(); for (int i = 0; i < newData.numInstances(); i++) { Instance one = newData.instance(i); one.setDataset(data); data.add(one); }