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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 290 · 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.📦 What happens during training? (a) it charges up (b) it prints labels (c) it learns patterns (d) it takes photos
  3. 3.⏩ In machine learning, which comes first: what training builds or naming a new fruit photo? (a) what training builds (b) naming a new fruit photo
  4. 4.Which of these is a prediction? (a) 500 labelled leaf photos (b) past weather records (c) flagging a new email (d) old emails marked spam
  5. 5.🔁 What is the right order? (a) data, prediction, model (b) prediction, data, model (c) data, model, prediction (d) model, prediction, data
  6. 6.True or false: “old emails marked spam” is training data. (a) True (b) False
  7. 7.🧪 Why test a model on NEW examples? (a) to delete its data (b) to change its name (c) to see if it works (d) to make it slower
  8. 8.True or false: “naming a new leaf” is the model. (a) True (b) False
  9. 9.True or false: “labelled fruit photos” is the model. (a) True (b) False
  10. 10.True or false: “naming a new fruit photo” is training data. (a) False (b) True
  11. 11.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  12. 12.True or false: “recorded spoken words” is the model. (a) True (b) False
  13. 13.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
  14. 14.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
  15. 15.Which of these is a prediction? (a) 500 labelled leaf photos (b) naming a new leaf (c) what training builds (d) recorded spoken words
  16. 16.True or false: “old emails marked spam” is the model. (a) False (b) True
  17. 17.Which of these is training data? (a) flagging a new email (b) labelled fruit photos (c) naming a new fruit photo (d) the trained program
  18. 18.⏩ In machine learning, which comes first: naming a new leaf or 500 labelled leaf photos? (a) naming a new leaf (b) 500 labelled leaf photos
  19. 19.True or false: “past weather records” is the model. (a) False (b) True
  20. 20.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  21. 21.Which of these is the model? (a) past weather records (b) what training builds (c) typing a new spoken word (d) tomorrow’s rain guess
  22. 22.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
  23. 23.True or false: “what training builds” is the model. (a) False (b) True
  24. 24.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
  25. 25.Which of these is the model? (a) the learned patterns (b) naming a new leaf (c) recorded spoken words (d) flagging a new email
  26. 26.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
  27. 27.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  28. 28.⏩ In machine learning, which comes first: typing a new spoken word or the trained program? (a) typing a new spoken word (b) the trained program
  29. 29.Which of these is a prediction? (a) tomorrow’s rain guess (b) what training builds (c) labelled fruit photos (d) past weather records
  30. 30.⏩ In machine learning, which comes first: the trained program or past weather records? (a) the trained program (b) past weather records
  31. 31.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  32. 32.Which of these is training data? (a) flagging a new email (b) the learned patterns (c) 500 labelled leaf photos (d) the trained program
  33. 33.True or false: “flagging a new email” is a prediction. (a) True (b) False
  34. 34.⏩ In machine learning, which comes first: naming a new fruit photo or past weather records? (a) naming a new fruit photo (b) past weather records
  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.Which of these is training data? (a) recorded spoken words (b) the learned patterns (c) what training builds (d) flagging a new email
  37. 37.True or false: “the trained program” is a prediction. (a) True (b) False
  38. 38.⏩ In machine learning, which comes first: past weather records or tomorrow’s rain guess? (a) past weather records (b) tomorrow’s rain guess
  39. 39.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  40. 40.True or false: “the trained program” is the model. (a) True (b) False

Answer key

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

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