Examples of weka.classifiers.Evaluation.precision()

weka.classifiers.Evaluation.precision()
Calculate the precision with respect to a particular class. This is defined as
```
 correctly classified positives ------------------------------ total predicted as positive 
```
@param classIndex the index of the class to consider as "positive" @return the precision

    result[current++] = new Double(eval.numFalsePositives(m_IRclass));
    result[current++] = new Double(eval.trueNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats

View Full Code Here

    result[current++] = new Double(eval.numFalsePositives(m_IRclass));
    result[current++] = new Double(eval.trueNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats

View Full Code Here

    // not clear which class we should use in that case... so
    // instead
    // we only print these metrics for binary classification
    // problems.
    output += "\tROC:" + evalModel.areaUnderROC(1);
    output += "\tPREC:" + evalModel.precision(1);
    output += "\tFSCR:" + evalModel.fMeasure(1);
  }
  System.out.println(output);
      }
    }

View Full Code Here

    result[current++] = new Double(eval.numFalsePositives(m_IRclass));
    result[current++] = new Double(eval.trueNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Timing stats

View Full Code Here

    result[current++] = new Double(eval.numFalsePositives(m_IRclass));
    result[current++] = new Double(eval.trueNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numTrueNegatives(m_IRclass));
    result[current++] = new Double(eval.falseNegativeRate(m_IRclass));
    result[current++] = new Double(eval.numFalseNegatives(m_IRclass));
    result[current++] = new Double(eval.precision(m_IRclass));
    result[current++] = new Double(eval.recall(m_IRclass));
    result[current++] = new Double(eval.fMeasure(m_IRclass));
    result[current++] = new Double(eval.areaUnderROC(m_IRclass));
    
    // Weighted IR stats

View Full Code Here

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.