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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 531 · 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) prediction, data, model (b) data, prediction, model (c) data, model, prediction (d) model, prediction, data
  2. 2.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  3. 3.⏩ In machine learning, which comes first: what training builds or past weather records? (a) what training builds (b) past weather records
  4. 4.Which of these is the model? (a) naming a new fruit photo (b) the learned patterns (c) labelled fruit photos (d) tomorrow’s rain guess
  5. 5.Which of these is the model? (a) naming a new leaf (b) the trained program (c) flagging a new email (d) recorded spoken words
  6. 6.⏩ In machine learning, which comes first: recorded spoken words or the trained program? (a) recorded spoken words (b) the trained program
  7. 7.Which of these is a prediction? (a) recorded spoken words (b) past weather records (c) the trained program (d) tomorrow’s rain guess
  8. 8.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) a prediction (c) the model
  9. 9.Which of these is the model? (a) typing a new spoken word (b) flagging a new email (c) past weather records (d) what training builds
  10. 10.Which of these is a prediction? (a) the trained program (b) 500 labelled leaf photos (c) typing a new spoken word (d) old emails marked spam
  11. 11.True or false: “the learned patterns” is a prediction. (a) False (b) True
  12. 12.Which of these is a prediction? (a) 500 labelled leaf photos (b) flagging a new email (c) old emails marked spam (d) what training builds
  13. 13.True or false: “recorded spoken words” is training data. (a) False (b) True
  14. 14.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
  15. 15.Which of these is training data? (a) typing a new spoken word (b) past weather records (c) tomorrow’s rain guess (d) the trained program
  16. 16.🧪 Why test a model on NEW examples? (a) to see if it works (b) to make it slower (c) to delete its data (d) to change its name
  17. 17.True or false: “naming a new leaf” is training data. (a) True (b) False
  18. 18.True or false: “naming a new leaf” is a prediction. (a) True (b) False
  19. 19.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  20. 20.True or false: “past weather records” is training data. (a) True (b) False
  21. 21.Which of these is training data? (a) the trained program (b) naming a new leaf (c) typing a new spoken word (d) old emails marked spam
  22. 22.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  23. 23.⏩ In machine learning, which comes first: past weather records or what training builds? (a) past weather records (b) what training builds
  24. 24.📦 What happens during training? (a) it learns patterns (b) it prints labels (c) it takes photos (d) it charges up
  25. 25.True or false: “500 labelled leaf photos” is a prediction. (a) True (b) False
  26. 26.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  27. 27.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  28. 28.Which of these is a prediction? (a) naming a new leaf (b) labelled fruit photos (c) the learned patterns (d) old emails marked spam
  29. 29.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  30. 30.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  31. 31.Which of these is training data? (a) what training builds (b) labelled fruit photos (c) naming a new fruit photo (d) typing a new spoken word
  32. 32.⏩ In machine learning, which comes first: the trained program or recorded spoken words? (a) the trained program (b) recorded spoken words
  33. 33.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) training data (c) a password (d) a label error
  34. 34.⏩ In machine learning, which comes first: past weather records or the trained program? (a) past weather records (b) the trained program
  35. 35.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  36. 36.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  37. 37.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) training data (c) the model
  38. 38.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
  39. 39.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) training data (c) a prediction
  40. 40.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) training data (c) a prediction

Answer key

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

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