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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 731 · 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: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  2. 2.Which of these is a prediction? (a) past weather records (b) recorded spoken words (c) naming a new leaf (d) what training builds
  3. 3.⏩ In machine learning, which comes first: naming a new fruit photo or the trained program? (a) naming a new fruit photo (b) the trained program
  4. 4.True or false: “flagging a new email” is training data. (a) True (b) False
  5. 5.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  6. 6.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  7. 7.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) training data (c) a prediction
  8. 8.True or false: “naming a new leaf” is a prediction. (a) True (b) False
  9. 9.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  10. 10.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) training data (c) the model
  11. 11.Which of these is the model? (a) recorded spoken words (b) the learned patterns (c) typing a new spoken word (d) flagging a new email
  12. 12.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  13. 13.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  14. 14.True or false: “labelled fruit photos” is the model. (a) False (b) True
  15. 15.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, prediction, model (d) data, model, prediction
  16. 16.True or false: “the learned patterns” is the model. (a) True (b) False
  17. 17.True or false: “flagging a new email” is the model. (a) False (b) True
  18. 18.⏩ In machine learning, which comes first: 500 labelled leaf photos or the trained program? (a) 500 labelled leaf photos (b) the trained program
  19. 19.Which of these is training data? (a) flagging a new email (b) the trained program (c) recorded spoken words (d) what training builds
  20. 20.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a password (c) training data (d) a prediction
  21. 21.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  22. 22.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  23. 23.Which of these is training data? (a) flagging a new email (b) the trained program (c) the learned patterns (d) past weather records
  24. 24.⏩ In machine learning, which comes first: recorded spoken words or typing a new spoken word? (a) recorded spoken words (b) typing a new spoken word
  25. 25.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program
  26. 26.Which of these is training data? (a) flagging a new email (b) naming a new fruit photo (c) 500 labelled leaf photos (d) naming a new leaf
  27. 27.Which of these is a prediction? (a) old emails marked spam (b) naming a new fruit photo (c) the trained program (d) what training builds
  28. 28.Which of these is the model? (a) typing a new spoken word (b) tomorrow’s rain guess (c) the trained program (d) past weather records
  29. 29.Which of these is training data? (a) the learned patterns (b) old emails marked spam (c) what training builds (d) the trained program
  30. 30.True or false: “recorded spoken words” is a prediction. (a) False (b) True
  31. 31.⏩ In machine learning, which comes first: 500 labelled leaf photos or the learned patterns? (a) 500 labelled leaf photos (b) the learned patterns
  32. 32.Which of these is a prediction? (a) recorded spoken words (b) flagging a new email (c) labelled fruit photos (d) what training builds
  33. 33.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  34. 34.⏩ In machine learning, which comes first: the learned patterns or 500 labelled leaf photos? (a) the learned patterns (b) 500 labelled leaf photos
  35. 35.⏩ In machine learning, which comes first: naming a new leaf or past weather records? (a) naming a new leaf (b) past weather records
  36. 36.True or false: “the trained program” is training data. (a) True (b) False
  37. 37.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  38. 38.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  39. 39.📦 What happens during training? (a) it learns patterns (b) it takes photos (c) it prints labels (d) it charges up
  40. 40.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam

Answer key

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

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