What are true positive, false positive, true negative, false negative in the given example?
Assuming cat as a positive class,

Confusion Matrix: 


  TN | FP
     FN | TP



True Positive(TP): Images which are cat and actually predicted cat
i.e. 25
True Negative(TN): Images which are not-cat and actually predicted not-cat
i.e. 50
False Positive(FP): Images which are not-cat and actually predicted as cat
i.e. 20
False Negative(FN): Images which are cat and actually predicted as not-cat
i.e. 5



  Precision:  TP/(TP+FP)
  Recall:  TP/(TP+FN)


Precision: 25/(25+20) = 0.55,
Recall: 25/(25+5) = 0.833