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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 228 · 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.Which of these is a prediction? (a) past weather records (b) what training builds (c) old emails marked spam (d) naming a new leaf
  2. 2.Which of these is training data? (a) the trained program (b) the learned patterns (c) recorded spoken words (d) flagging a new email
  3. 3.⏩ 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
  4. 4.⏩ 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
  5. 5.Which of these is a prediction? (a) old emails marked spam (b) the learned patterns (c) tomorrow’s rain guess (d) labelled fruit photos
  6. 6.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a label error (c) training data (d) a password
  7. 7.True or false: “the learned patterns” is the model. (a) False (b) True
  8. 8.⏩ In machine learning, which comes first: what training builds or naming a new fruit photo? (a) what training builds (b) naming a new fruit photo
  9. 9.True or false: “typing a new spoken word” is training data. (a) True (b) False
  10. 10.⏩ In machine learning, which comes first: past weather records or naming a new leaf? (a) past weather records (b) naming a new leaf
  11. 11.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) training data (c) a prediction
  12. 12.True or false: “past weather records” is training data. (a) False (b) True
  13. 13.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  14. 14.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it learns patterns (d) it charges up
  15. 15.⏩ In machine learning, which comes first: labelled fruit photos or the learned patterns? (a) labelled fruit photos (b) the learned patterns
  16. 16.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  17. 17.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  18. 18.True or false: “labelled fruit photos” is training data. (a) True (b) False
  19. 19.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  20. 20.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) the model (c) a prediction
  21. 21.⏩ In machine learning, which comes first: tomorrow’s rain guess or the learned patterns? (a) tomorrow’s rain guess (b) the learned patterns
  22. 22.Which of these is training data? (a) tomorrow’s rain guess (b) the trained program (c) flagging a new email (d) labelled fruit photos
  23. 23.🔁 What is the right order? (a) data, prediction, model (b) model, prediction, data (c) prediction, data, model (d) data, model, prediction
  24. 24.🧪 Why test a model on NEW examples? (a) to delete its data (b) to change its name (c) to see if it works (d) to make it slower
  25. 25.True or false: “flagging a new email” is training data. (a) True (b) False
  26. 26.Which of these is a prediction? (a) naming a new fruit photo (b) labelled fruit photos (c) the learned patterns (d) past weather records
  27. 27.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  28. 28.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  29. 29.Which of these is the model? (a) typing a new spoken word (b) the learned patterns (c) past weather records (d) 500 labelled leaf photos
  30. 30.True or false: “recorded spoken words” is training data. (a) False (b) True
  31. 31.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) a prediction (c) the model
  32. 32.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
  33. 33.True or false: “typing a new spoken word” is the model. (a) False (b) True
  34. 34.True or false: “the trained program” is training data. (a) True (b) False
  35. 35.Which of these is a prediction? (a) what training builds (b) flagging a new email (c) recorded spoken words (d) labelled fruit photos
  36. 36.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
  37. 37.True or false: “old emails marked spam” is the model. (a) True (b) False
  38. 38.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  39. 39.True or false: “tomorrow’s rain guess” is the model. (a) False (b) True
  40. 40.Which of these is training data? (a) naming a new fruit photo (b) the learned patterns (c) flagging a new email (d) past weather records

Answer key

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

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