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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 320 · 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 happens during training? (a) it takes photos (b) it charges up (c) it prints labels (d) it learns patterns
  2. 2.True or false: “what training builds” is training data. (a) False (b) True
  3. 3.Which of these is the model? (a) 500 labelled leaf photos (b) what training builds (c) recorded spoken words (d) labelled fruit photos
  4. 4.Which of these is training data? (a) typing a new spoken word (b) the trained program (c) what training builds (d) recorded spoken words
  5. 5.Which of these is a prediction? (a) labelled fruit photos (b) 500 labelled leaf photos (c) the learned patterns (d) flagging a new email
  6. 6.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  7. 7.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) training data (c) the model
  8. 8.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  9. 9.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  10. 10.Which of these is the model? (a) 500 labelled leaf photos (b) typing a new spoken word (c) the learned patterns (d) flagging a new email
  11. 11.🧪 Why test a model on NEW examples? (a) to make it slower (b) to change its name (c) to delete its data (d) to see if it works
  12. 12.Which of these is training data? (a) the trained program (b) naming a new leaf (c) old emails marked spam (d) naming a new fruit photo
  13. 13.True or false: “the learned patterns” is a prediction. (a) False (b) True
  14. 14.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  15. 15.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) the model (c) a prediction
  16. 16.True or false: “the learned patterns” is training data. (a) False (b) True
  17. 17.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  18. 18.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  19. 19.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  20. 20.Which of these is training data? (a) the trained program (b) tomorrow’s rain guess (c) the learned patterns (d) past weather records
  21. 21.True or false: “old emails marked spam” is the model. (a) False (b) True
  22. 22.Which of these is training data? (a) what training builds (b) 500 labelled leaf photos (c) naming a new fruit photo (d) tomorrow’s rain guess
  23. 23.Which of these is a prediction? (a) what training builds (b) past weather records (c) old emails marked spam (d) naming a new leaf
  24. 24.True or false: “the trained program” is training data. (a) True (b) False
  25. 25.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  26. 26.🔁 What is the right order? (a) model, prediction, data (b) data, model, prediction (c) data, prediction, model (d) prediction, data, model
  27. 27.⏩ In machine learning, which comes first: past weather records or the learned patterns? (a) past weather records (b) the learned patterns
  28. 28.⏩ In machine learning, which comes first: typing a new spoken word or 500 labelled leaf photos? (a) typing a new spoken word (b) 500 labelled leaf photos
  29. 29.⏩ In machine learning, which comes first: recorded spoken words or flagging a new email? (a) recorded spoken words (b) flagging a new email
  30. 30.⏩ 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
  31. 31.Which of these is the model? (a) the trained program (b) tomorrow’s rain guess (c) 500 labelled leaf photos (d) old emails marked spam
  32. 32.🔮 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
  33. 33.True or false: “labelled fruit photos” is training data. (a) False (b) True
  34. 34.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
  35. 35.⏩ In machine learning, which comes first: past weather records or the trained program? (a) past weather records (b) the trained program
  36. 36.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) a prediction (c) the model
  37. 37.Which of these is training data? (a) what training builds (b) typing a new spoken word (c) labelled fruit photos (d) tomorrow’s rain guess
  38. 38.⏩ In machine learning, which comes first: naming a new leaf or labelled fruit photos? (a) naming a new leaf (b) labelled fruit photos
  39. 39.True or false: “the trained program” is a prediction. (a) True (b) False
  40. 40.⏩ In machine learning, which comes first: typing a new spoken word or old emails marked spam? (a) typing a new spoken word (b) old emails marked spam

Answer key

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

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