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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 203 · 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.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) training data (c) a prediction
  2. 2.📦 What happens during training? (a) it charges up (b) it takes photos (c) it prints labels (d) it learns patterns
  3. 3.Which of these is training data? (a) the trained program (b) tomorrow’s rain guess (c) naming a new leaf (d) 500 labelled leaf photos
  4. 4.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a prediction (c) a password (d) a label error
  5. 5.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  6. 6.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  7. 7.Which of these is a prediction? (a) recorded spoken words (b) what training builds (c) naming a new fruit photo (d) old emails marked spam
  8. 8.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  9. 9.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  10. 10.🧪 Why test a model on NEW examples? (a) to change its name (b) to delete its data (c) to make it slower (d) to see if it works
  11. 11.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  12. 12.True or false: “naming a new leaf” is training data. (a) False (b) True
  13. 13.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  14. 14.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  15. 15.⏩ In machine learning, which comes first: tomorrow’s rain guess or what training builds? (a) tomorrow’s rain guess (b) what training builds
  16. 16.⏩ 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
  17. 17.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data
  18. 18.⏩ In machine learning, which comes first: naming a new leaf or recorded spoken words? (a) naming a new leaf (b) recorded spoken words
  19. 19.⏩ In machine learning, which comes first: labelled fruit photos or tomorrow’s rain guess? (a) labelled fruit photos (b) tomorrow’s rain guess
  20. 20.True or false: “flagging a new email” is a prediction. (a) True (b) False
  21. 21.⏩ 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
  22. 22.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  23. 23.True or false: “the trained program” is the model. (a) False (b) True
  24. 24.Which of these is training data? (a) naming a new leaf (b) old emails marked spam (c) the trained program (d) what training builds
  25. 25.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  26. 26.True or false: “past weather records” is the model. (a) True (b) False
  27. 27.⏩ In machine learning, which comes first: the learned patterns or labelled fruit photos? (a) the learned patterns (b) labelled fruit photos
  28. 28.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) training data (c) the model
  29. 29.True or false: “old emails marked spam” is training data. (a) True (b) False
  30. 30.True or false: “past weather records” is training data. (a) False (b) True
  31. 31.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) training data (c) a prediction
  32. 32.Which of these is a prediction? (a) past weather records (b) the learned patterns (c) recorded spoken words (d) typing a new spoken word
  33. 33.Which of these is training data? (a) naming a new fruit photo (b) what training builds (c) the trained program (d) past weather records
  34. 34.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  35. 35.🔁 What is the right order? (a) data, model, prediction (b) data, prediction, model (c) prediction, data, model (d) model, prediction, data
  36. 36.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) training data (c) the model
  37. 37.⏩ In machine learning, which comes first: typing a new spoken word or labelled fruit photos? (a) typing a new spoken word (b) labelled fruit photos
  38. 38.⏩ In machine learning, which comes first: 500 labelled leaf photos or what training builds? (a) 500 labelled leaf photos (b) what training builds
  39. 39.True or false: “flagging a new email” is training data. (a) False (b) True
  40. 40.True or false: “recorded spoken words” is training data. (a) False (b) True

Answer key

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

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