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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 770 · 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: “recorded spoken words” is training data. (a) False (b) True
  2. 2.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  3. 3.True or false: “past weather records” is the model. (a) False (b) True
  4. 4.📦 What happens during training? (a) it prints labels (b) it learns patterns (c) it takes photos (d) it charges up
  5. 5.⏩ In machine learning, which comes first: naming a new fruit photo or old emails marked spam? (a) naming a new fruit photo (b) old emails marked spam
  6. 6.Which of these is training data? (a) the trained program (b) naming a new fruit photo (c) 500 labelled leaf photos (d) naming a new leaf
  7. 7.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) model, prediction, data (d) prediction, data, model
  8. 8.True or false: “the trained program” is training data. (a) False (b) True
  9. 9.Which of these is training data? (a) typing a new spoken word (b) tomorrow’s rain guess (c) labelled fruit photos (d) flagging a new email
  10. 10.True or false: “what training builds” is training data. (a) False (b) True
  11. 11.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  12. 12.Which of these is a prediction? (a) recorded spoken words (b) typing a new spoken word (c) the learned patterns (d) 500 labelled leaf photos
  13. 13.Which of these is the model? (a) flagging a new email (b) the trained program (c) 500 labelled leaf photos (d) past weather records
  14. 14.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  15. 15.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) a password (d) training data
  16. 16.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  17. 17.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  18. 18.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  19. 19.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  20. 20.⏩ 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
  21. 21.True or false: “recorded spoken words” is the model. (a) False (b) True
  22. 22.⏩ In machine learning, which comes first: tomorrow’s rain guess or labelled fruit photos? (a) tomorrow’s rain guess (b) labelled fruit photos
  23. 23.True or false: “flagging a new email” is training data. (a) True (b) False
  24. 24.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  25. 25.⏩ In machine learning, which comes first: labelled fruit photos or naming a new fruit photo? (a) labelled fruit photos (b) naming a new fruit photo
  26. 26.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  27. 27.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  28. 28.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  29. 29.True or false: “naming a new fruit photo” is the model. (a) False (b) True
  30. 30.Which of these is training data? (a) recorded spoken words (b) naming a new leaf (c) flagging a new email (d) typing a new spoken word
  31. 31.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
  32. 32.Which of these is the model? (a) tomorrow’s rain guess (b) flagging a new email (c) the learned patterns (d) 500 labelled leaf photos
  33. 33.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
  34. 34.Which of these is the model? (a) typing a new spoken word (b) labelled fruit photos (c) 500 labelled leaf photos (d) what training builds
  35. 35.Which of these is a prediction? (a) old emails marked spam (b) 500 labelled leaf photos (c) labelled fruit photos (d) tomorrow’s rain guess
  36. 36.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  37. 37.True or false: “what training builds” is the model. (a) True (b) False
  38. 38.🧪 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
  39. 39.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  40. 40.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) the model (c) a prediction

Answer key

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

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