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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 722 · 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: “old emails marked spam” is training data. (a) True (b) False
  2. 2.📦 What happens during training? (a) it learns patterns (b) it prints labels (c) it takes photos (d) it charges up
  3. 3.Which of these is the model? (a) what training builds (b) past weather records (c) old emails marked spam (d) flagging a new email
  4. 4.True or false: “flagging a new email” is training data. (a) False (b) True
  5. 5.Which of these is a prediction? (a) labelled fruit photos (b) naming a new fruit photo (c) recorded spoken words (d) 500 labelled leaf photos
  6. 6.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  7. 7.🧪 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
  8. 8.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  9. 9.⏩ In machine learning, which comes first: the learned patterns or typing a new spoken word? (a) the learned patterns (b) typing a new spoken word
  10. 10.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
  11. 11.Which of these is the model? (a) naming a new leaf (b) the learned patterns (c) old emails marked spam (d) naming a new fruit photo
  12. 12.Which of these is the model? (a) 500 labelled leaf photos (b) the trained program (c) old emails marked spam (d) labelled fruit photos
  13. 13.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  14. 14.True or false: “500 labelled leaf photos” is a prediction. (a) True (b) False
  15. 15.True or false: “flagging a new email” is a prediction. (a) True (b) False
  16. 16.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  17. 17.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
  18. 18.Which of these is a prediction? (a) old emails marked spam (b) tomorrow’s rain guess (c) past weather records (d) recorded spoken words
  19. 19.Which of these is training data? (a) the trained program (b) the learned patterns (c) labelled fruit photos (d) flagging a new email
  20. 20.Which of these is training data? (a) naming a new leaf (b) the learned patterns (c) old emails marked spam (d) what training builds
  21. 21.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) a prediction (b) training data (c) the model
  22. 22.True or false: “naming a new leaf” is training data. (a) True (b) False
  23. 23.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  24. 24.⏩ In machine learning, which comes first: naming a new leaf or old emails marked spam? (a) naming a new leaf (b) old emails marked spam
  25. 25.🔮 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
  26. 26.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  27. 27.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) a prediction (c) training data
  28. 28.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) a prediction (c) training data
  29. 29.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  30. 30.⏩ In machine learning, which comes first: old emails marked spam or tomorrow’s rain guess? (a) old emails marked spam (b) tomorrow’s rain guess
  31. 31.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, model, prediction (d) data, prediction, model
  32. 32.⏩ In machine learning, which comes first: typing a new spoken word or the trained program? (a) typing a new spoken word (b) the trained program
  33. 33.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  34. 34.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  35. 35.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  36. 36.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  37. 37.Which of these is a prediction? (a) recorded spoken words (b) labelled fruit photos (c) naming a new leaf (d) past weather records
  38. 38.⏩ In machine learning, which comes first: flagging a new email or old emails marked spam? (a) flagging a new email (b) old emails marked spam
  39. 39.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  40. 40.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns

Answer key

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

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