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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 311 · 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.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) prediction, data, model (d) model, prediction, data
  2. 2.📦 What happens during training? (a) it takes photos (b) it charges up (c) it prints labels (d) it learns patterns
  3. 3.True or false: “recorded spoken words” is the model. (a) False (b) True
  4. 4.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  5. 5.⏩ 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
  6. 6.True or false: “flagging a new email” is a prediction. (a) False (b) True
  7. 7.Which of these is the model? (a) naming a new fruit photo (b) the trained program (c) naming a new leaf (d) tomorrow’s rain guess
  8. 8.🧪 Why test a model on NEW examples? (a) to see if it works (b) to change its name (c) to delete its data (d) to make it slower
  9. 9.Which of these is the model? (a) typing a new spoken word (b) naming a new fruit photo (c) recorded spoken words (d) the learned patterns
  10. 10.True or false: “past weather records” is the model. (a) False (b) True
  11. 11.Which of these is the model? (a) what training builds (b) typing a new spoken word (c) recorded spoken words (d) old emails marked spam
  12. 12.Which of these is a prediction? (a) old emails marked spam (b) the learned patterns (c) the trained program (d) tomorrow’s rain guess
  13. 13.Which of these is a prediction? (a) recorded spoken words (b) labelled fruit photos (c) what training builds (d) naming a new leaf
  14. 14.Which of these is training data? (a) labelled fruit photos (b) naming a new leaf (c) what training builds (d) the learned patterns
  15. 15.⏩ In machine learning, which comes first: flagging a new email or what training builds? (a) flagging a new email (b) what training builds
  16. 16.⏩ In machine learning, which comes first: tomorrow’s rain guess or 500 labelled leaf photos? (a) tomorrow’s rain guess (b) 500 labelled leaf photos
  17. 17.True or false: “the learned patterns” is the model. (a) True (b) False
  18. 18.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  19. 19.True or false: “what training builds” is training data. (a) True (b) False
  20. 20.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  21. 21.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  22. 22.⏩ In machine learning, which comes first: the trained program or naming a new fruit photo? (a) the trained program (b) naming a new fruit photo
  23. 23.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) a prediction (c) training data
  24. 24.Which of these is a prediction? (a) past weather records (b) naming a new fruit photo (c) 500 labelled leaf photos (d) the trained program
  25. 25.⏩ In machine learning, which comes first: old emails marked spam or the learned patterns? (a) old emails marked spam (b) the learned patterns
  26. 26.True or false: “labelled fruit photos” is the model. (a) False (b) True
  27. 27.True or false: “recorded spoken words” is a prediction. (a) False (b) True
  28. 28.Which of these is training data? (a) tomorrow’s rain guess (b) recorded spoken words (c) the trained program (d) naming a new leaf
  29. 29.True or false: “naming a new leaf” is training data. (a) False (b) True
  30. 30.⏩ In machine learning, which comes first: recorded spoken words or flagging a new email? (a) recorded spoken words (b) flagging a new email
  31. 31.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a password (c) a prediction (d) training data
  32. 32.⏩ In machine learning, which comes first: the trained program or typing a new spoken word? (a) the trained program (b) typing a new spoken word
  33. 33.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  34. 34.Which of these is training data? (a) old emails marked spam (b) what training builds (c) naming a new leaf (d) the learned patterns
  35. 35.True or false: “past weather records” is training data. (a) True (b) False
  36. 36.Which of these is a prediction? (a) typing a new spoken word (b) what training builds (c) labelled fruit photos (d) recorded spoken words
  37. 37.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
  38. 38.True or false: “the trained program” is training data. (a) True (b) False
  39. 39.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  40. 40.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False

Answer key

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

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