/** * Calls toMatrixString() with a default title. * * @return the confusion matrix as a string * @throws Exception if the class is numeric */ public String toMatrixString() throws Exception { return toMatrixString("=== Confusion Matrix ===\n"); }
/** * Calls toMatrixString() with a default title. * * @return the confusion matrix as a string * @throws Exception if the class is numeric */ public String toMatrixString() throws Exception { return m_delegate.toMatrixString(); }
/** * Outputs the performance statistics as a classification confusion matrix. * For each class value, shows the distribution of predicted class values. * * @param title the title for the confusion matrix * @return the confusion matrix as a String * @throws Exception if the class is numeric */ public String toMatrixString(String title) throws Exception { return m_delegate.toMatrixString(title); }
/** * Calls toMatrixString() with a default title. * * @return the confusion matrix as a string * @throws Exception if the class is numeric */ public String toMatrixString() throws Exception { return m_delegate.toMatrixString(); }
/** * Outputs the performance statistics as a classification confusion matrix. * For each class value, shows the distribution of predicted class values. * * @param title the title for the confusion matrix * @return the confusion matrix as a String * @throws Exception if the class is numeric */ public String toMatrixString(String title) throws Exception { return m_delegate.toMatrixString(title); }
/** * Calls toMatrixString() with a default title. * * @return the confusion matrix as a string * @throws Exception if the class is numeric */ public String toMatrixString() throws Exception { return toMatrixString("=== Confusion Matrix ===\n"); }
//set the class index dataFiltered.setClassIndex(dataFiltered.numAttributes() - 1); //build a model -- choose a classifier as you want classifier.buildClassifier(dataFiltered); Evaluation eval = new Evaluation(dataFiltered); eval.crossValidateModel(classifier, dataFiltered, 10, new Random(1)); //print stats -- do not require to calculate confusion mtx, weka do it! System.out.println(classifier); System.out.println(eval.toSummaryString()); System.out.println(eval.toMatrixString()); System.out.println(eval.toClassDetailsString());
Instances trainData = ds.getDataset(); //get training dataset SMO sm = new SMO(); //build classifier sm.buildClassifier(data); //train classifier Instances testData = ds.getDataSet(); //now get the test set Evaluation eval = new Evaluation(data); //for recording results eval.evaluateModel(sm, testData); System.out.println(eval.toMatrixString()); //gives the confusion matrix for predictions
System.out.println(eval.toMatrixString()); txtAreaShow.append(eval.toMatrixString()); txtAreaShow.append("\n\n\n");
text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString()); text.append("\n\n" + testingEvaluation.toClassDetailsString()); text.append("\n\n" + testingEvaluation.toMatrixString()); text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString()); text.append("\n\n" + testingEvaluation.toClassDetailsString()); text.append("\n\n" + testingEvaluation.toMatrixString()); text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString()); text.append("\n\n" + testingEvaluation.toClassDetailsString()); text.append("\n\n" + testingEvaluation.toMatrixString()); text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString());
text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString()); text.append("\n\n" + testingEvaluation.toClassDetailsString()); text.append("\n\n" + testingEvaluation.toMatrixString()); text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString()); text.append("\n\n" + testingEvaluation.toClassDetailsString()); text.append("\n\n" + testingEvaluation.toMatrixString()); text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString()); text.append("\n\n" + testingEvaluation.toClassDetailsString()); text.append("\n\n" + testingEvaluation.toMatrixString()); text.append("\n\n" + trainingEvaluation.toClassDetailsString()); text.append("\n\n" + trainingEvaluation.toMatrixString());
for (String line : eval.toMatrixString().split("\n")) { logger.info(line);
results += "\n" + m_eval.toMatrixString();
results += "\n" + m_eval.toMatrixString();