actual predicted true positives (tpfalse positives (fpfalse negatives (fntrue negatives (tnconfusion matrix in the confusion matrix above is for positive class and is for negative class following are the terms associated with confusion matrixtrue positivestps are the cases when the actual class of data point was and the predicted is also true negativestns are the cases when the actual class of the data point was and the predicted is also false positivesfps are the cases when the actual class of data point was and the predicted is also false negativesfns are the cases when the actual class of the data point was and the predicted is also accuracy the confusion matrix itself is not performance measure as such but almost all the performance matrices are based on the confusion matrix one of them is accuracy in classification problemsit may be defined as the number of correct predictions made by the model over all kinds of predictions made the formula for calculating the accuracy is as followsaccuracy tp tn tp fp fn tn precision it is mostly used in document retrievals it may be defined as how many of the returned documents are correct following is the formula for calculating the precisionprecision tp tp fp recall or sensitivity it may be defined as how many of the positives do the model return following is the formula for calculating the recall/sensitivity of the modelrecall tp tp fn