/** * Output a representation of this classifier * * @return a string representation of the classifier */ public String toString() { if (m_Classifier == null) { return "CostSensitiveClassifier: No model built yet."; } String result = "CostSensitiveClassifier using "; if (m_MinimizeExpectedCost) { result += "minimized expected misclasification cost\n"; } else { result += "reweighted training instances\n"; } result += "\n" + getClassifierSpec() + "\n\nClassifier Model\n" + m_Classifier.toString() + "\n\nCost Matrix\n" + m_CostMatrix.toString(); return result; }
/** * Output a representation of this classifier * * @return a string representation of the classifier */ public String toString() { if (m_Classifier == null) { return "CostSensitiveClassifier: No model built yet."; } String result = "CostSensitiveClassifier using "; if (m_MinimizeExpectedCost) { result += "minimized expected misclasification cost\n"; } else { result += "reweighted training instances\n"; } result += "\n" + getClassifierSpec() + "\n\nClassifier Model\n" + m_Classifier.toString() + "\n\nCost Matrix\n" + m_CostMatrix.toString(); return result; }
text.append(costMatrix.toString());
text.append(costMatrix.toString());
.append(costMatrix.toString()).append("\n");
+ ((textOptions.length() > 0) ? "Options: " + textOptions + "\n" : "") + "Relation: " + testData.relationName() + "\n\n" + (cm != null ? "Cost matrix:\n" + cm.toString() + "\n" : "") + m_eval.toSummaryString();
.append(costMatrix.toString()).append("\n");
+ ((textOptions.length() > 0) ? "Options: " + textOptions + "\n" : "") + "Relation: " + testData.relationName() + "\n\n" + (cm != null ? "Cost matrix:\n" + cm.toString() + "\n" : "") + m_eval.toSummaryString();