**Attribute Name** |
**Definition** |

Correctly Classified |
Displays the percentage of correctness test that how many instances are categorized accurately. |

Incorrectly classified |
Displays the percentage of incorrectness test that how many instances are categorized accurately. |

TP Rate |
Those which were true and classified as True. |

FP Rate |
Those which were false but classified as True. |

ROC Rate |
ROC graph is a technique for visualizing, organizing and selecting classifiers based on their performance.ROC graphs have long beenused in signal detection theory. |

Precision |
Calculating precision and recall is actually quite easy. When you get the actual results you sum up how many times you were right or wrong |

Types of Precision
TN
TP
FN
FP |
There are four ways of being right or wrong:
case was negative and predicted negative
case was positive and predicted positive
case was positive but predicted negative
case was negative but predicted positive |

Accuracy |
A measure of a predictive model that reflects the proportionatenumber of times that the modelis correct when applied to data. |

Error Rate |
A number that reflects the rate of errors made by a predictive model. It is one minus the accuracy. |