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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 435 · 40 questions
TalentJR

AI tip (Data → model → prediction): Training data = the examples (500 labelled leaf photos, old emails marked spam). The model = the learned patterns that training builds. A prediction = the model’s answer about something NEW (naming a new leaf, flagging a new email). The order is always data → model → prediction.

NameDateTime takenScore ___ / 40
  1. 1.True or false: “the trained program” is a prediction. (a) True (b) False
  2. 2.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, prediction, model (d) data, model, prediction
  3. 3.⏩ In machine learning, which comes first: what training builds or naming a new fruit photo? (a) what training builds (b) naming a new fruit photo
  4. 4.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data
  5. 5.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  6. 6.True or false: “naming a new leaf” is the model. (a) True (b) False
  7. 7.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) the model (c) training data
  8. 8.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  9. 9.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it charges up (d) it learns patterns
  10. 10.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
  11. 11.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) a prediction (c) the model
  12. 12.⏩ In machine learning, which comes first: past weather records or naming a new fruit photo? (a) past weather records (b) naming a new fruit photo
  13. 13.Which of these is a prediction? (a) old emails marked spam (b) what training builds (c) recorded spoken words (d) flagging a new email
  14. 14.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  15. 15.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) a prediction (c) training data
  16. 16.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a password (c) a prediction (d) training data
  17. 17.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  18. 18.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) the model (c) a prediction
  19. 19.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) training data (c) a prediction
  20. 20.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
  21. 21.⏩ In machine learning, which comes first: typing a new spoken word or the trained program? (a) typing a new spoken word (b) the trained program
  22. 22.Which of these is the model? (a) naming a new leaf (b) old emails marked spam (c) what training builds (d) 500 labelled leaf photos
  23. 23.⏩ In machine learning, which comes first: naming a new leaf or old emails marked spam? (a) naming a new leaf (b) old emails marked spam
  24. 24.True or false: “old emails marked spam” is training data. (a) True (b) False
  25. 25.⏩ In machine learning, which comes first: flagging a new email or old emails marked spam? (a) flagging a new email (b) old emails marked spam
  26. 26.⏩ In machine learning, which comes first: old emails marked spam or what training builds? (a) old emails marked spam (b) what training builds
  27. 27.⏩ In machine learning, which comes first: tomorrow’s rain guess or recorded spoken words? (a) tomorrow’s rain guess (b) recorded spoken words
  28. 28.True or false: “what training builds” is the model. (a) True (b) False
  29. 29.True or false: “past weather records” is training data. (a) True (b) False
  30. 30.⏩ In machine learning, which comes first: the learned patterns or labelled fruit photos? (a) the learned patterns (b) labelled fruit photos
  31. 31.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  32. 32.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  33. 33.True or false: “flagging a new email” is a prediction. (a) False (b) True
  34. 34.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  35. 35.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
  36. 36.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
  37. 37.Which of these is the model? (a) recorded spoken words (b) labelled fruit photos (c) the learned patterns (d) old emails marked spam
  38. 38.Which of these is a prediction? (a) past weather records (b) what training builds (c) labelled fruit photos (d) tomorrow’s rain guess
  39. 39.True or false: “the learned patterns” is training data. (a) True (b) False
  40. 40.⏩ In machine learning, which comes first: labelled fruit photos or typing a new spoken word? (a) labelled fruit photos (b) typing a new spoken word

Answer key

  1. False
  2. data, model, prediction
  3. what training builds
  4. training data
  5. True
  6. False
  7. a prediction
  8. the model
  9. it learns patterns
  10. the learned patterns
  11. training data
  12. past weather records
  13. flagging a new email
  14. True
  15. a prediction
  16. a prediction
  17. training data
  18. the model
  19. the model
  20. training data
  21. the trained program
  22. what training builds
  23. old emails marked spam
  24. True
  25. old emails marked spam
  26. old emails marked spam
  27. recorded spoken words
  28. True
  29. True
  30. labelled fruit photos
  31. True
  32. a prediction
  33. True
  34. False
  35. the trained program
  36. a prediction
  37. the learned patterns
  38. tomorrow’s rain guess
  39. False
  40. labelled fruit photos

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