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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 356 · 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: “past weather records”? (a) the model (b) training data (c) a prediction
  2. 2.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) a prediction (c) training data
  3. 3.True or false: “what training builds” is a prediction. (a) False (b) True
  4. 4.Which of these is training data? (a) flagging a new email (b) typing a new spoken word (c) tomorrow’s rain guess (d) 500 labelled leaf photos
  5. 5.True or false: “the trained program” is training data. (a) False (b) True
  6. 6.🧪 Why test a model on NEW examples? (a) to see if it works (b) to make it slower (c) to delete its data (d) to change its name
  7. 7.True or false: “labelled fruit photos” is training data. (a) False (b) True
  8. 8.📦 What happens during training? (a) it charges up (b) it prints labels (c) it takes photos (d) it learns patterns
  9. 9.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  10. 10.⏩ In machine learning, which comes first: typing a new spoken word or the learned patterns? (a) typing a new spoken word (b) the learned patterns
  11. 11.⏩ In machine learning, which comes first: the learned patterns or labelled fruit photos? (a) the learned patterns (b) labelled fruit photos
  12. 12.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) a prediction (c) training data
  13. 13.⏩ In machine learning, which comes first: naming a new leaf or recorded spoken words? (a) naming a new leaf (b) recorded spoken words
  14. 14.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) a prediction (c) the model
  15. 15.True or false: “naming a new fruit photo” is the model. (a) False (b) True
  16. 16.🔁 What is the right order? (a) data, model, prediction (b) prediction, data, model (c) data, prediction, model (d) model, prediction, data
  17. 17.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  18. 18.True or false: “what training builds” is the model. (a) False (b) True
  19. 19.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) the model (c) training data
  20. 20.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
  21. 21.True or false: “typing a new spoken word” is training data. (a) False (b) True
  22. 22.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  23. 23.Which of these is the model? (a) the trained program (b) flagging a new email (c) 500 labelled leaf photos (d) labelled fruit photos
  24. 24.True or false: “the trained program” is the model. (a) True (b) False
  25. 25.⏩ In machine learning, which comes first: 500 labelled leaf photos or the learned patterns? (a) 500 labelled leaf photos (b) the learned patterns
  26. 26.⏩ In machine learning, which comes first: recorded spoken words or naming a new fruit photo? (a) recorded spoken words (b) naming a new fruit photo
  27. 27.True or false: “flagging a new email” is training data. (a) False (b) True
  28. 28.Which of these is training data? (a) the trained program (b) old emails marked spam (c) the learned patterns (d) flagging a new email
  29. 29.Which of these is a prediction? (a) the trained program (b) old emails marked spam (c) naming a new leaf (d) the learned patterns
  30. 30.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  31. 31.⏩ 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
  32. 32.Which of these is training data? (a) the learned patterns (b) naming a new leaf (c) flagging a new email (d) labelled fruit photos
  33. 33.True or false: “old emails marked spam” is training data. (a) True (b) False
  34. 34.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  35. 35.Which of these is the model? (a) naming a new fruit photo (b) labelled fruit photos (c) naming a new leaf (d) what training builds
  36. 36.True or false: “past weather records” is the model. (a) True (b) False
  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) tomorrow’s rain guess (b) past weather records (c) the learned patterns (d) the trained program
  39. 39.⏩ In machine learning, which comes first: past weather records or tomorrow’s rain guess? (a) past weather records (b) tomorrow’s rain guess
  40. 40.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data

Answer key

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

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