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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 970 · 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.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  2. 2.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  3. 3.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  4. 4.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) training data (c) the model
  5. 5.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) a prediction (c) the model
  6. 6.True or false: “old emails marked spam” is training data. (a) True (b) False
  7. 7.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  8. 8.True or false: “past weather records” is a prediction. (a) False (b) True
  9. 9.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) the model (c) training data
  10. 10.True or false: “the learned patterns” is a prediction. (a) False (b) True
  11. 11.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, prediction, model (d) data, model, prediction
  12. 12.⏩ In machine learning, which comes first: naming a new leaf or labelled fruit photos? (a) naming a new leaf (b) labelled fruit photos
  13. 13.True or false: “flagging a new email” is a prediction. (a) True (b) False
  14. 14.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  15. 15.🧪 Why test a model on NEW examples? (a) to see if it works (b) to make it slower (c) to change its name (d) to delete its data
  16. 16.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a prediction (c) a password (d) a label error
  17. 17.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  18. 18.Which of these is a prediction? (a) what training builds (b) the trained program (c) old emails marked spam (d) naming a new leaf
  19. 19.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  20. 20.True or false: “what training builds” is training data. (a) False (b) True
  21. 21.Which of these is training data? (a) the trained program (b) flagging a new email (c) past weather records (d) the learned patterns
  22. 22.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program
  23. 23.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  24. 24.Which of these is the model? (a) recorded spoken words (b) 500 labelled leaf photos (c) flagging a new email (d) the learned patterns
  25. 25.⏩ 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
  26. 26.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) the model (c) a prediction
  27. 27.Which of these is training data? (a) labelled fruit photos (b) the learned patterns (c) the trained program (d) flagging a new email
  28. 28.⏩ In machine learning, which comes first: past weather records or naming a new leaf? (a) past weather records (b) naming a new leaf
  29. 29.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  30. 30.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) the model (c) training data
  31. 31.📦 What happens during training? (a) it takes photos (b) it charges up (c) it learns patterns (d) it prints labels
  32. 32.True or false: “the trained program” is the model. (a) False (b) True
  33. 33.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  34. 34.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns
  35. 35.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  36. 36.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  37. 37.True or false: “labelled fruit photos” is the model. (a) True (b) False
  38. 38.Which of these is the model? (a) past weather records (b) what training builds (c) tomorrow’s rain guess (d) flagging a new email
  39. 39.⏩ In machine learning, which comes first: recorded spoken words or the trained program? (a) recorded spoken words (b) the trained program
  40. 40.True or false: “old emails marked spam” is a prediction. (a) False (b) True

Answer key

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

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