Measure

Precision is defined as the number of true positive (TP) predictions, divided by the sum of the number of true positives and false positives (TP+FP): $$\text{Precision}=\frac{tp}{tp+fp} \, $$ It is also referred to as the Positive predictive value (PPV). See: http://en.wikipedia.org/wiki/Precision_and_recall Precision is defined only for a specific class value, and should thus be labeled with the class value for which is was computed. Use the mean_weighted_precision for the weighted average over all class values.

Source Code:

WEKA's Evaluation.precision(int classIndex) /** * 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 */ public double precision(int classIndex) { double correct = 0, total = 0; for (int i = 0; i < m_NumClasses; i++) { if (i == classIndex) { correct += m_ConfusionMatrix[i][classIndex]; } total += m_ConfusionMatrix[i][classIndex]; } if (total == 0) { return 0; } return correct / total; }

Minimum value | 0 |

Maximum value | 0 |

Unit | |

Optimization | Higher is better |