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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 893 · 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, model, prediction (b) model, prediction, data (c) prediction, data, model (d) data, prediction, model
  2. 2.Which of these is a prediction? (a) the learned patterns (b) flagging a new email (c) labelled fruit photos (d) past weather records
  3. 3.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  4. 4.Which of these is the model? (a) the trained program (b) past weather records (c) old emails marked spam (d) naming a new fruit photo
  5. 5.⏩ In machine learning, which comes first: the trained program or typing a new spoken word? (a) the trained program (b) typing a new spoken word
  6. 6.True or false: “old emails marked spam” is training data. (a) True (b) False
  7. 7.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  8. 8.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  9. 9.True or false: “the learned patterns” is training data. (a) True (b) False
  10. 10.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  11. 11.True or false: “past weather records” is training data. (a) True (b) False
  12. 12.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) a prediction (c) training data
  13. 13.True or false: “the trained program” is the model. (a) False (b) True
  14. 14.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  15. 15.Which of these is the model? (a) tomorrow’s rain guess (b) 500 labelled leaf photos (c) what training builds (d) flagging a new email
  16. 16.True or false: “tomorrow’s rain guess” is the model. (a) True (b) False
  17. 17.⏩ In machine learning, which comes first: the learned patterns or labelled fruit photos? (a) the learned patterns (b) labelled fruit photos
  18. 18.Which of these is training data? (a) the learned patterns (b) flagging a new email (c) labelled fruit photos (d) typing a new spoken word
  19. 19.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) a label error (c) a prediction (d) training data
  20. 20.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  21. 21.⏩ In machine learning, which comes first: old emails marked spam or naming a new leaf? (a) old emails marked spam (b) naming a new leaf
  22. 22.⏩ In machine learning, which comes first: flagging a new email or 500 labelled leaf photos? (a) flagging a new email (b) 500 labelled leaf photos
  23. 23.⏩ In machine learning, which comes first: naming a new fruit photo or the trained program? (a) naming a new fruit photo (b) the trained program
  24. 24.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  25. 25.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it prints labels (d) it charges up
  26. 26.True or false: “labelled fruit photos” is training data. (a) False (b) True
  27. 27.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  28. 28.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  29. 29.Which of these is the model? (a) naming a new fruit photo (b) the learned patterns (c) 500 labelled leaf photos (d) past weather records
  30. 30.⏩ In machine learning, which comes first: what training builds or flagging a new email? (a) what training builds (b) flagging a new email
  31. 31.Which of these is training data? (a) naming a new fruit photo (b) the trained program (c) the learned patterns (d) 500 labelled leaf photos
  32. 32.Which of these is training data? (a) old emails marked spam (b) flagging a new email (c) typing a new spoken word (d) naming a new leaf
  33. 33.⏩ In machine learning, which comes first: naming a new fruit photo or labelled fruit photos? (a) naming a new fruit photo (b) labelled fruit photos
  34. 34.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  35. 35.⏩ In machine learning, which comes first: old emails marked spam or typing a new spoken word? (a) old emails marked spam (b) typing a new spoken word
  36. 36.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  37. 37.🧪 Why test a model on NEW examples? (a) to see if it works (b) to delete its data (c) to change its name (d) to make it slower
  38. 38.True or false: “recorded spoken words” is training data. (a) True (b) False
  39. 39.⏩ In machine learning, which comes first: tomorrow’s rain guess or what training builds? (a) tomorrow’s rain guess (b) what training builds
  40. 40.⏩ In machine learning, which comes first: old emails marked spam or what training builds? (a) old emails marked spam (b) what training builds

Answer key

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

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