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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 702 · 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: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  2. 2.⏩ In machine learning, which comes first: old emails marked spam or naming a new leaf? (a) old emails marked spam (b) naming a new leaf
  3. 3.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  4. 4.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) model, prediction, data (d) prediction, data, model
  5. 5.Which of these is the model? (a) flagging a new email (b) recorded spoken words (c) old emails marked spam (d) the learned patterns
  6. 6.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) training data (c) the model
  7. 7.True or false: “labelled fruit photos” is training data. (a) False (b) True
  8. 8.True or false: “past weather records” is training data. (a) False (b) True
  9. 9.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  10. 10.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  11. 11.Which of these is training data? (a) naming a new fruit photo (b) typing a new spoken word (c) naming a new leaf (d) past weather records
  12. 12.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) training data (c) a prediction
  13. 13.Which of these is a prediction? (a) what training builds (b) naming a new fruit photo (c) past weather records (d) recorded spoken words
  14. 14.Which of these is the model? (a) typing a new spoken word (b) naming a new leaf (c) recorded spoken words (d) the trained program
  15. 15.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  16. 16.Which of these is training data? (a) naming a new leaf (b) flagging a new email (c) the trained program (d) 500 labelled leaf photos
  17. 17.True or false: “flagging a new email” is a prediction. (a) False (b) True
  18. 18.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  19. 19.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  20. 20.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a password (c) training data (d) a prediction
  21. 21.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  22. 22.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
  23. 23.⏩ In machine learning, which comes first: naming a new fruit photo or the learned patterns? (a) naming a new fruit photo (b) the learned patterns
  24. 24.Which of these is a prediction? (a) labelled fruit photos (b) the learned patterns (c) past weather records (d) naming a new leaf
  25. 25.🧪 Why test a model on NEW examples? (a) to change its name (b) to see if it works (c) to delete its data (d) to make it slower
  26. 26.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  27. 27.True or false: “past weather records” is a prediction. (a) True (b) False
  28. 28.⏩ In machine learning, which comes first: 500 labelled leaf photos or the trained program? (a) 500 labelled leaf photos (b) the trained program
  29. 29.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  30. 30.True or false: “naming a new fruit photo” is training data. (a) False (b) True
  31. 31.⏩ In machine learning, which comes first: recorded spoken words or the trained program? (a) recorded spoken words (b) the trained program
  32. 32.Which of these is the model? (a) recorded spoken words (b) tomorrow’s rain guess (c) past weather records (d) what training builds
  33. 33.⏩ In machine learning, which comes first: tomorrow’s rain guess or recorded spoken words? (a) tomorrow’s rain guess (b) recorded spoken words
  34. 34.True or false: “labelled fruit photos” is a prediction. (a) True (b) False
  35. 35.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
  36. 36.⏩ In machine learning, which comes first: typing a new spoken word or what training builds? (a) typing a new spoken word (b) what training builds
  37. 37.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) a prediction (c) training data
  38. 38.Which of these is a prediction? (a) the trained program (b) 500 labelled leaf photos (c) the learned patterns (d) tomorrow’s rain guess
  39. 39.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  40. 40.True or false: “past weather records” is the model. (a) True (b) False

Answer key

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

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