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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 677 · 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: “tomorrow’s rain guess” is the model. (a) True (b) False
  2. 2.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  3. 3.True or false: “the trained program” is training data. (a) True (b) False
  4. 4.Which of these is training data? (a) the trained program (b) the learned patterns (c) what training builds (d) labelled fruit photos
  5. 5.Which of these is a prediction? (a) the trained program (b) what training builds (c) naming a new fruit photo (d) past weather records
  6. 6.⏩ In machine learning, which comes first: old emails marked spam or naming a new fruit photo? (a) old emails marked spam (b) naming a new fruit photo
  7. 7.Which of these is a prediction? (a) naming a new leaf (b) old emails marked spam (c) the trained program (d) what training builds
  8. 8.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  9. 9.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  10. 10.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) training data (c) the model
  11. 11.⏩ 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
  12. 12.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns
  13. 13.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a password (c) training data (d) a label error
  14. 14.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  15. 15.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, model, prediction (d) data, prediction, model
  16. 16.🧪 Why test a model on NEW examples? (a) to delete its data (b) to make it slower (c) to change its name (d) to see if it works
  17. 17.⏩ In machine learning, which comes first: 500 labelled leaf photos or the learned patterns? (a) 500 labelled leaf photos (b) the learned patterns
  18. 18.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  19. 19.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  20. 20.True or false: “what training builds” is the model. (a) False (b) True
  21. 21.⏩ 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
  22. 22.Which of these is the model? (a) labelled fruit photos (b) what training builds (c) old emails marked spam (d) 500 labelled leaf photos
  23. 23.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) the model (c) a prediction
  24. 24.True or false: “tomorrow’s rain guess” is training data. (a) False (b) True
  25. 25.📦 What happens during training? (a) it learns patterns (b) it prints labels (c) it takes photos (d) it charges up
  26. 26.⏩ In machine learning, which comes first: labelled fruit photos or typing a new spoken word? (a) labelled fruit photos (b) typing a new spoken word
  27. 27.True or false: “past weather records” is a prediction. (a) True (b) False
  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.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  30. 30.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  31. 31.⏩ In machine learning, which comes first: flagging a new email or what training builds? (a) flagging a new email (b) what training builds
  32. 32.True or false: “old emails marked spam” is the model. (a) False (b) True
  33. 33.True or false: “flagging a new email” is training data. (a) False (b) True
  34. 34.Which of these is training data? (a) naming a new leaf (b) past weather records (c) flagging a new email (d) what training builds
  35. 35.True or false: “typing a new spoken word” is the model. (a) False (b) True
  36. 36.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) the model (c) training data
  37. 37.Which of these is a prediction? (a) 500 labelled leaf photos (b) typing a new spoken word (c) recorded spoken words (d) the trained program
  38. 38.⏩ In machine learning, which comes first: what training builds or typing a new spoken word? (a) what training builds (b) typing a new spoken word
  39. 39.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  40. 40.True or false: “500 labelled leaf photos” is training data. (a) True (b) False

Answer key

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

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