What are some metrics on which NLP models are evaluated?

1.	What are some metrics on which NLP models are evaluated?
Answer» The following are some metrics on which NLP models are evaluated: Accuracy: When the output variable is categorical or discrete, accuracy is used. It is the percentage of correct predictions made by the model compared to the TOTAL number of predictions made. PRECISION: Indicates how precise or EXACT the model's predictions are, i.e., how MANY positive (the class we care about) examples can the model correctly identify given all of them? Recall: Precision and recall are complementary. It measures how effectively the model can recall the positive class, i.e., how many of the positive predictions it generates are correct. F1 score: This metric COMBINES precision and recall into a single metric that also represents the trade-off between accuracy and recall, i.e., completeness and exactness. (2 Precision Recall) / (Precision + Recall) is the formula for F1. AUC: As the prediction threshold is changed, the AUC captures the number of correct positive predictions versus the number of incorrect positive predictions.

Answer»

The following are some metrics on which NLP models are evaluated:

Accuracy: When the output variable is categorical or discrete, accuracy is used. It is the percentage of correct predictions made by the model compared to the TOTAL number of predictions made.
PRECISION: Indicates how precise or EXACT the model's predictions are, i.e., how MANY positive (the class we care about) examples can the model correctly identify given all of them?
Recall: Precision and recall are complementary. It measures how effectively the model can recall the positive class, i.e., how many of the positive predictions it generates are correct.
F1 score: This metric COMBINES precision and recall into a single metric that also represents the trade-off between accuracy and recall, i.e., completeness and exactness.
(2 Precision Recall) / (Precision + Recall) is the formula for F1.
AUC: As the prediction threshold is changed, the AUC captures the number of correct positive predictions versus the number of incorrect positive predictions.

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