统计检验中的评估指标

• True Positives(TP) : The number of positive instances that were classified as positive.
• True Negatives(TN) : The number of negative instances that were classified as negative.
• False Positives(FP) : The number of negtive instances that were classified as positive.
• False Negatives(FN) : The number of  positive instances that were classified as negitve.

1. 准确性是整体的分类性能的最标准的指标

   The accuracy is the most stand metric to summarize the over 
   all classification performance for all classes and it is 
   defined as follows:
   

   

2. 精度（阳性预测值）是正确分类为阳性实例（TP）与分类为阳性实例总数（TP + FP）的比例

   The precision, often referred as positive predictive value, 
   is the ratio of correctly classified positive instances to 
   the total number of instances classified as positive.

   

3. 召回率（真阳性）是正确分类的阳性实例（TP）与阳性实例总数（TP+FN）的比率

   The recall, also called true positive rate is the ratio of correctly classified
   positive instances to the total number of positive instances.

   

4. F值：结合精度和召回率的一个值

   The F-measure combines precision and recall in a single value.