Performance metrics

https://medium.com/usf-msds/choosing-the-right-metric-for-evaluating-machine-learning-models-part-2-86d5649a5428

Classification metrics

Accuracy = (Number of correct predictions) / (Total number of predictions)
for binary classification Accuracy = (TP + TN) / (TP+TN+FP+FN).
Accuracy doesn’t work well with a class-imbalanced data set.

Precision = TP / (TP + FP). Tells what proportion of positive identifications was actually correct.

Recall = TP (TP + FN). Tells what proportion of actual positives was identified correctly.

Read more about precision and recall here.

Specifity = TN / (TN + FP). In the case of cancer diagnosis task, it tells what proportion of patients that did NOT have cancer, were predicted by the model as non-cancerous.

Language processing metrics

Levenshtein distance – a distance between two words in terms of the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.

Perplexity – a measurement of how well a probability distribution or probability model predicts a sample. It may be used to compare probability models. A low perplexity indicates the probability distribution is good at predicting the sample.