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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 791 · 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 happens during training? (a) it learns patterns (b) it takes photos (c) it charges up (d) it prints labels
  2. 2.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
  3. 3.⏩ In machine learning, which comes first: past weather records or naming a new fruit photo? (a) past weather records (b) naming a new fruit photo
  4. 4.🧪 Why test a model on NEW examples? (a) to see if it works (b) to delete its data (c) to change its name (d) to make it slower
  5. 5.Which of these is the model? (a) recorded spoken words (b) flagging a new email (c) the learned patterns (d) naming a new fruit photo
  6. 6.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  7. 7.Which of these is a prediction? (a) the trained program (b) 500 labelled leaf photos (c) the learned patterns (d) typing a new spoken word
  8. 8.Which of these is the model? (a) recorded spoken words (b) 500 labelled leaf photos (c) the trained program (d) typing a new spoken word
  9. 9.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  10. 10.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  11. 11.⏩ In machine learning, which comes first: recorded spoken words or the trained program? (a) recorded spoken words (b) the trained program
  12. 12.True or false: “what training builds” is a prediction. (a) False (b) True
  13. 13.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) a prediction (c) training data
  14. 14.True or false: “labelled fruit photos” is training data. (a) False (b) True
  15. 15.True or false: “flagging a new email” is a prediction. (a) True (b) False
  16. 16.⏩ In machine learning, which comes first: naming a new fruit photo or what training builds? (a) naming a new fruit photo (b) what training builds
  17. 17.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) a password (d) training data
  18. 18.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  19. 19.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns
  20. 20.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  21. 21.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  22. 22.⏩ In machine learning, which comes first: the learned patterns or labelled fruit photos? (a) the learned patterns (b) labelled fruit photos
  23. 23.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  24. 24.Which of these is training data? (a) what training builds (b) the learned patterns (c) old emails marked spam (d) the trained program
  25. 25.Which of these is training data? (a) flagging a new email (b) naming a new fruit photo (c) tomorrow’s rain guess (d) labelled fruit photos
  26. 26.True or false: “naming a new leaf” is training data. (a) False (b) True
  27. 27.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) data, model, prediction (d) prediction, data, model
  28. 28.True or false: “labelled fruit photos” is the model. (a) True (b) False
  29. 29.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) training data (c) a prediction
  30. 30.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) a prediction (c) the model
  31. 31.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  32. 32.Which of these is training data? (a) past weather records (b) the trained program (c) typing a new spoken word (d) tomorrow’s rain guess
  33. 33.True or false: “flagging a new email” is the model. (a) False (b) True
  34. 34.True or false: “typing a new spoken word” is the model. (a) True (b) False
  35. 35.Which of these is a prediction? (a) recorded spoken words (b) naming a new leaf (c) 500 labelled leaf photos (d) old emails marked spam
  36. 36.Which of these is the model? (a) old emails marked spam (b) past weather records (c) labelled fruit photos (d) what training builds
  37. 37.⏩ In machine learning, which comes first: tomorrow’s rain guess or the trained program? (a) tomorrow’s rain guess (b) the trained program
  38. 38.Which of these is a prediction? (a) past weather records (b) naming a new fruit photo (c) the trained program (d) what training builds
  39. 39.⏩ In machine learning, which comes first: 500 labelled leaf photos or naming a new leaf? (a) 500 labelled leaf photos (b) naming a new leaf
  40. 40.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program

Answer key

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

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