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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 442 · 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: typing a new spoken word or what training builds? (a) typing a new spoken word (b) what training builds
  2. 2.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  3. 3.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new fruit photo (c) old emails marked spam (d) what training builds
  4. 4.⏩ In machine learning, which comes first: labelled fruit photos or naming a new fruit photo? (a) labelled fruit photos (b) naming a new fruit photo
  5. 5.True or false: “what training builds” is training data. (a) False (b) True
  6. 6.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  7. 7.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) the model (c) training data
  8. 8.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  9. 9.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) prediction, data, model (d) data, model, prediction
  10. 10.True or false: “recorded spoken words” is training data. (a) False (b) True
  11. 11.📦 What happens during training? (a) it takes photos (b) it prints labels (c) it charges up (d) it learns patterns
  12. 12.Which of these is training data? (a) naming a new leaf (b) 500 labelled leaf photos (c) the learned patterns (d) flagging a new email
  13. 13.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  14. 14.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a label error (c) training data (d) a password
  15. 15.⏩ In machine learning, which comes first: old emails marked spam or flagging a new email? (a) old emails marked spam (b) flagging a new email
  16. 16.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
  17. 17.⏩ In machine learning, which comes first: flagging a new email or old emails marked spam? (a) flagging a new email (b) old emails marked spam
  18. 18.⏩ In machine learning, which comes first: naming a new leaf or recorded spoken words? (a) naming a new leaf (b) recorded spoken words
  19. 19.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  20. 20.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) training data (c) the model
  21. 21.⏩ In machine learning, which comes first: tomorrow’s rain guess or the learned patterns? (a) tomorrow’s rain guess (b) the learned patterns
  22. 22.True or false: “tomorrow’s rain guess” is training data. (a) False (b) True
  23. 23.True or false: “past weather records” is the model. (a) False (b) True
  24. 24.Which of these is the model? (a) recorded spoken words (b) the trained program (c) old emails marked spam (d) labelled fruit photos
  25. 25.True or false: “what training builds” is the model. (a) False (b) True
  26. 26.True or false: “flagging a new email” is a prediction. (a) True (b) False
  27. 27.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) the model (c) a prediction
  28. 28.Which of these is the model? (a) old emails marked spam (b) 500 labelled leaf photos (c) what training builds (d) typing a new spoken word
  29. 29.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  30. 30.⏩ In machine learning, which comes first: 500 labelled leaf photos or the trained program? (a) 500 labelled leaf photos (b) the trained program
  31. 31.True or false: “the learned patterns” is training data. (a) False (b) True
  32. 32.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  33. 33.Which of these is training data? (a) past weather records (b) the trained program (c) the learned patterns (d) tomorrow’s rain guess
  34. 34.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  35. 35.True or false: “naming a new leaf” is the model. (a) False (b) True
  36. 36.True or false: “past weather records” is training data. (a) True (b) False
  37. 37.Which of these is a prediction? (a) 500 labelled leaf photos (b) the learned patterns (c) labelled fruit photos (d) naming a new fruit photo
  38. 38.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  39. 39.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  40. 40.⏩ In machine learning, which comes first: the learned patterns or 500 labelled leaf photos? (a) the learned patterns (b) 500 labelled leaf photos

Answer key

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

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