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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 235 · 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: “typing a new spoken word” is the model. (a) True (b) False
  2. 2.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) a prediction (c) training data
  3. 3.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) a prediction (c) training data
  4. 4.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  5. 5.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
  6. 6.⏩ 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
  7. 7.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) the model (c) a prediction
  8. 8.Which of these is training data? (a) naming a new fruit photo (b) typing a new spoken word (c) the learned patterns (d) recorded spoken words
  9. 9.Which of these is a prediction? (a) recorded spoken words (b) the learned patterns (c) naming a new fruit photo (d) 500 labelled leaf photos
  10. 10.Which of these is the model? (a) typing a new spoken word (b) old emails marked spam (c) recorded spoken words (d) what training builds
  11. 11.⏩ In machine learning, which comes first: 500 labelled leaf photos or naming a new fruit photo? (a) 500 labelled leaf photos (b) naming a new fruit photo
  12. 12.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) the model (c) training data
  13. 13.📦 What happens during training? (a) it takes photos (b) it charges up (c) it learns patterns (d) it prints labels
  14. 14.Which of these is training data? (a) naming a new leaf (b) tomorrow’s rain guess (c) 500 labelled leaf photos (d) naming a new fruit photo
  15. 15.True or false: “old emails marked spam” is the model. (a) True (b) False
  16. 16.Which of these is training data? (a) labelled fruit photos (b) naming a new fruit photo (c) flagging a new email (d) the trained program
  17. 17.True or false: “naming a new leaf” is the model. (a) False (b) True
  18. 18.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) the model (c) training data
  19. 19.True or false: “recorded spoken words” is training data. (a) False (b) True
  20. 20.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  21. 21.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
  22. 22.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  23. 23.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) training data (c) a label error (d) a password
  24. 24.Which of these is a prediction? (a) recorded spoken words (b) the learned patterns (c) the trained program (d) naming a new leaf
  25. 25.True or false: “recorded spoken words” is the model. (a) False (b) True
  26. 26.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  27. 27.Which of these is training data? (a) flagging a new email (b) the trained program (c) typing a new spoken word (d) past weather records
  28. 28.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  29. 29.Which of these is training data? (a) typing a new spoken word (b) naming a new leaf (c) tomorrow’s rain guess (d) old emails marked spam
  30. 30.Which of these is the model? (a) tomorrow’s rain guess (b) old emails marked spam (c) the learned patterns (d) naming a new leaf
  31. 31.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) a prediction (c) training data
  32. 32.True or false: “past weather records” is the model. (a) False (b) True
  33. 33.True or false: “past weather records” is training data. (a) False (b) True
  34. 34.True or false: “the learned patterns” is the model. (a) True (b) False
  35. 35.⏩ In machine learning, which comes first: tomorrow’s rain guess or what training builds? (a) tomorrow’s rain guess (b) what training builds
  36. 36.True or false: “typing a new spoken word” is training data. (a) True (b) False
  37. 37.Which of these is the model? (a) typing a new spoken word (b) the trained program (c) tomorrow’s rain guess (d) old emails marked spam
  38. 38.True or false: “flagging a new email” is the model. (a) False (b) True
  39. 39.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) training data (c) the model
  40. 40.True or false: “old emails marked spam” is training data. (a) False (b) True

Answer key

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

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