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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 76 · 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.🔁 What is the right order? (a) data, prediction, model (b) model, prediction, data (c) prediction, data, model (d) data, model, prediction
  2. 2.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  3. 3.Which of these is the model? (a) flagging a new email (b) the trained program (c) naming a new leaf (d) labelled fruit photos
  4. 4.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) a prediction (c) training data
  5. 5.Which of these is the model? (a) what training builds (b) typing a new spoken word (c) flagging a new email (d) past weather records
  6. 6.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
  7. 7.⏩ In machine learning, which comes first: labelled fruit photos or the learned patterns? (a) labelled fruit photos (b) the learned patterns
  8. 8.True or false: “flagging a new email” is the model. (a) False (b) True
  9. 9.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a label error (c) a password (d) training data
  10. 10.📦 What happens during training? (a) it learns patterns (b) it prints labels (c) it takes photos (d) it charges up
  11. 11.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  12. 12.Which of these is training data? (a) the learned patterns (b) 500 labelled leaf photos (c) flagging a new email (d) naming a new fruit photo
  13. 13.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  14. 14.⏩ In machine learning, which comes first: naming a new fruit photo or 500 labelled leaf photos? (a) naming a new fruit photo (b) 500 labelled leaf photos
  15. 15.True or false: “old emails marked spam” is a prediction. (a) True (b) False
  16. 16.True or false: “the learned patterns” is a prediction. (a) False (b) True
  17. 17.⏩ In machine learning, which comes first: naming a new leaf or 500 labelled leaf photos? (a) naming a new leaf (b) 500 labelled leaf photos
  18. 18.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  19. 19.⏩ In machine learning, which comes first: the learned patterns or labelled fruit photos? (a) the learned patterns (b) labelled fruit photos
  20. 20.🧪 Why test a model on NEW examples? (a) to see if it works (b) to delete its data (c) to make it slower (d) to change its name
  21. 21.True or false: “the trained program” is the model. (a) False (b) True
  22. 22.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  23. 23.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
  24. 24.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new fruit photo (c) what training builds (d) labelled fruit photos
  25. 25.⏩ In machine learning, which comes first: tomorrow’s rain guess or past weather records? (a) tomorrow’s rain guess (b) past weather records
  26. 26.⏩ In machine learning, which comes first: the trained program or recorded spoken words? (a) the trained program (b) recorded spoken words
  27. 27.⏩ 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
  28. 28.⏩ In machine learning, which comes first: typing a new spoken word or recorded spoken words? (a) typing a new spoken word (b) recorded spoken words
  29. 29.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  30. 30.Which of these is training data? (a) typing a new spoken word (b) old emails marked spam (c) the trained program (d) what training builds
  31. 31.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  32. 32.⏩ In machine learning, which comes first: naming a new fruit photo or what training builds? (a) naming a new fruit photo (b) what training builds
  33. 33.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new leaf (c) naming a new fruit photo (d) past weather records
  34. 34.⏩ In machine learning, which comes first: 500 labelled leaf photos or typing a new spoken word? (a) 500 labelled leaf photos (b) typing a new spoken word
  35. 35.Which of these is a prediction? (a) old emails marked spam (b) typing a new spoken word (c) the learned patterns (d) past weather records
  36. 36.True or false: “typing a new spoken word” is training data. (a) True (b) False
  37. 37.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  38. 38.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  39. 39.⏩ In machine learning, which comes first: naming a new fruit photo or the learned patterns? (a) naming a new fruit photo (b) the learned patterns
  40. 40.True or false: “past weather records” is a prediction. (a) True (b) False

Answer key

  1. data, model, prediction
  2. old emails marked spam
  3. the trained program
  4. a prediction
  5. what training builds
  6. a prediction
  7. labelled fruit photos
  8. False
  9. a prediction
  10. it learns patterns
  11. the model
  12. 500 labelled leaf photos
  13. True
  14. 500 labelled leaf photos
  15. False
  16. False
  17. 500 labelled leaf photos
  18. True
  19. labelled fruit photos
  20. to see if it works
  21. True
  22. a prediction
  23. a prediction
  24. labelled fruit photos
  25. past weather records
  26. recorded spoken words
  27. past weather records
  28. recorded spoken words
  29. True
  30. old emails marked spam
  31. training data
  32. what training builds
  33. past weather records
  34. 500 labelled leaf photos
  35. typing a new spoken word
  36. False
  37. False
  38. the trained program
  39. the learned patterns
  40. False

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