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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 105 · 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: “labelled fruit photos” is training data. (a) False (b) True
  2. 2.📦 What happens during training? (a) it charges up (b) it takes photos (c) it prints labels (d) it learns patterns
  3. 3.⏩ 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
  4. 4.Which of these is training data? (a) the trained program (b) naming a new leaf (c) the learned patterns (d) past weather records
  5. 5.Which of these is a prediction? (a) old emails marked spam (b) what training builds (c) 500 labelled leaf photos (d) naming a new leaf
  6. 6.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  7. 7.True or false: “the learned patterns” is training data. (a) True (b) False
  8. 8.🧪 Why test a model on NEW examples? (a) to change its name (b) to make it slower (c) to see if it works (d) to delete its data
  9. 9.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  10. 10.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) training data (c) the model
  11. 11.Which of these is a prediction? (a) 500 labelled leaf photos (b) typing a new spoken word (c) old emails marked spam (d) the trained program
  12. 12.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) model, prediction, data (d) data, model, prediction
  13. 13.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  14. 14.True or false: “tomorrow’s rain guess” is training data. (a) True (b) False
  15. 15.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a password (c) a label error (d) a prediction
  16. 16.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  17. 17.True or false: “500 labelled leaf photos” is a prediction. (a) True (b) False
  18. 18.Which of these is training data? (a) the learned patterns (b) naming a new fruit photo (c) naming a new leaf (d) 500 labelled leaf photos
  19. 19.⏩ 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
  20. 20.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  21. 21.True or false: “tomorrow’s rain guess” is the model. (a) False (b) True
  22. 22.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) the model (c) training data
  23. 23.Which of these is training data? (a) typing a new spoken word (b) naming a new leaf (c) recorded spoken words (d) naming a new fruit photo
  24. 24.Which of these is the model? (a) typing a new spoken word (b) old emails marked spam (c) the trained program (d) tomorrow’s rain guess
  25. 25.⏩ In machine learning, which comes first: the learned patterns or recorded spoken words? (a) the learned patterns (b) recorded spoken words
  26. 26.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  27. 27.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  28. 28.⏩ In machine learning, which comes first: what training builds or labelled fruit photos? (a) what training builds (b) labelled fruit photos
  29. 29.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  30. 30.⏩ In machine learning, which comes first: naming a new fruit photo or what training builds? (a) naming a new fruit photo (b) what training builds
  31. 31.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  32. 32.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  33. 33.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) a prediction (c) the model
  34. 34.True or false: “the trained program” is a prediction. (a) False (b) True
  35. 35.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
  36. 36.True or false: “labelled fruit photos” is a prediction. (a) True (b) False
  37. 37.Which of these is training data? (a) the learned patterns (b) the trained program (c) tomorrow’s rain guess (d) labelled fruit photos
  38. 38.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
  39. 39.True or false: “labelled fruit photos” is the model. (a) True (b) False
  40. 40.⏩ 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

Answer key

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

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