Practice Evaluation Metrics For Nlp (9.8) - Natural Language Processing (NLP)

Practice: Evaluation Metrics for NLP

Lesson 9.8Natural Language Processing (NLP)

Practise this section

9 questions

Flashcard drill
5 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
Sign up to start the exercise
Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
Sign up to start the quiz

6 sample questions

Question 1.
What is the formula for calculating accuracy?
Show hintHide hint
Think about how many predictions your model made correctly.
Question 2.
Define precision in the context of model evaluation.
Show hintHide hint
Focus on how many positive predictions were actually correct.
3Question 3.
What does the accuracy of a model indicate?
- The proportion of correct predictions
- The number of true positives
- The overlap of n-grams
Show hintHide hint
Consider the total predictions made.
4Question 4.
True or False: A high precision score always indicates a good model.
- True
- False
Show hintHide hint
Think about the balance between precision and recall.
Question 5.
A classification model returns 100 total predictions: 80 true positives, 10 false positives, and 10 false negatives. Calculate the precision, recall, and F1-score.
Show hintHide hint
Use the formulas for precision, recall, and F1-score provided in class.
Question 6.
Consider a machine translation output with a BLEU score of 0.65. If the reference translation had an n-gram overlap of 60%, discuss whether this is an acceptable score and why.
Show hintHide hint
Evaluate the BLEU score compared to industry standards.

5 more questions in this section. Enroll free to practise them all.