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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 792 · 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.⏩ In machine learning, which comes first: 500 labelled leaf photos or flagging a new email? (a) 500 labelled leaf photos (b) flagging a new email
  2. 2.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  3. 3.Which of these is the model? (a) 500 labelled leaf photos (b) the trained program (c) typing a new spoken word (d) naming a new leaf
  4. 4.🔁 What is the right order? (a) data, prediction, model (b) model, prediction, data (c) prediction, data, model (d) data, model, prediction
  5. 5.📦 What happens during training? (a) it charges up (b) it learns patterns (c) it takes photos (d) it prints labels
  6. 6.True or false: “old emails marked spam” is the model. (a) False (b) True
  7. 7.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  8. 8.🧪 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
  9. 9.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  10. 10.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
  11. 11.True or false: “the learned patterns” is training data. (a) False (b) True
  12. 12.⏩ In machine learning, which comes first: the trained program or flagging a new email? (a) the trained program (b) flagging a new email
  13. 13.True or false: “flagging a new email” is the model. (a) True (b) False
  14. 14.True or false: “past weather records” is the model. (a) True (b) False
  15. 15.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) a prediction (c) the model
  16. 16.Which of these is training data? (a) tomorrow’s rain guess (b) typing a new spoken word (c) the learned patterns (d) 500 labelled leaf photos
  17. 17.⏩ In machine learning, which comes first: naming a new fruit photo or labelled fruit photos? (a) naming a new fruit photo (b) labelled fruit photos
  18. 18.Which of these is a prediction? (a) typing a new spoken word (b) 500 labelled leaf photos (c) recorded spoken words (d) old emails marked spam
  19. 19.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  20. 20.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  21. 21.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  22. 22.True or false: “the trained program” is a prediction. (a) True (b) False
  23. 23.Which of these is the model? (a) 500 labelled leaf photos (b) tomorrow’s rain guess (c) what training builds (d) naming a new leaf
  24. 24.True or false: “labelled fruit photos” is the model. (a) False (b) True
  25. 25.Which of these is training data? (a) what training builds (b) the learned patterns (c) old emails marked spam (d) the trained program
  26. 26.Which of these is the model? (a) flagging a new email (b) the learned patterns (c) typing a new spoken word (d) old emails marked spam
  27. 27.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  28. 28.True or false: “naming a new leaf” is training data. (a) True (b) False
  29. 29.⏩ In machine learning, which comes first: 500 labelled leaf photos or naming a new fruit photo? (a) 500 labelled leaf photos (b) naming a new fruit photo
  30. 30.Which of these is a prediction? (a) the learned patterns (b) 500 labelled leaf photos (c) the trained program (d) tomorrow’s rain guess
  31. 31.Which of these is training data? (a) tomorrow’s rain guess (b) labelled fruit photos (c) flagging a new email (d) naming a new leaf
  32. 32.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) the model (c) training data
  33. 33.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  34. 34.Which of these is a prediction? (a) naming a new fruit photo (b) old emails marked spam (c) the learned patterns (d) the trained program
  35. 35.True or false: “typing a new spoken word” is training data. (a) True (b) False
  36. 36.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) a prediction (b) the model (c) training data
  37. 37.Which of these is training data? (a) past weather records (b) typing a new spoken word (c) tomorrow’s rain guess (d) what training builds
  38. 38.True or false: “labelled fruit photos” is training data. (a) True (b) False
  39. 39.Which of these is a prediction? (a) labelled fruit photos (b) the trained program (c) naming a new leaf (d) past weather records
  40. 40.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) a prediction (c) training data

Answer key

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

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