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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 537 · 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) training data (c) a prediction
  2. 2.⏩ In machine learning, which comes first: what training builds or flagging a new email? (a) what training builds (b) flagging a new email
  3. 3.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) prediction, data, model (d) model, prediction, data
  4. 4.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  5. 5.Which of these is the model? (a) labelled fruit photos (b) tomorrow’s rain guess (c) the learned patterns (d) past weather records
  6. 6.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) a prediction (c) the model
  7. 7.⏩ 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
  8. 8.True or false: “past weather records” is training data. (a) True (b) False
  9. 9.⏩ In machine learning, which comes first: old emails marked spam or the learned patterns? (a) old emails marked spam (b) the learned patterns
  10. 10.🧪 Why test a model on NEW examples? (a) to delete its data (b) to change its name (c) to make it slower (d) to see if it works
  11. 11.True or false: “the learned patterns” is the model. (a) False (b) True
  12. 12.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  13. 13.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
  14. 14.True or false: “recorded spoken words” is training data. (a) False (b) True
  15. 15.Which of these is training data? (a) naming a new fruit photo (b) typing a new spoken word (c) old emails marked spam (d) tomorrow’s rain guess
  16. 16.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
  17. 17.⏩ In machine learning, which comes first: the learned patterns or typing a new spoken word? (a) the learned patterns (b) typing a new spoken word
  18. 18.True or false: “labelled fruit photos” is the model. (a) True (b) False
  19. 19.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
  20. 20.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  21. 21.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) a password (d) training data
  22. 22.📦 What happens during training? (a) it prints labels (b) it charges up (c) it takes photos (d) it learns patterns
  23. 23.⏩ 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
  24. 24.Which of these is the model? (a) naming a new leaf (b) labelled fruit photos (c) 500 labelled leaf photos (d) what training builds
  25. 25.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  26. 26.True or false: “old emails marked spam” is the model. (a) True (b) False
  27. 27.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  28. 28.Which of these is training data? (a) labelled fruit photos (b) tomorrow’s rain guess (c) naming a new fruit photo (d) naming a new leaf
  29. 29.Which of these is a prediction? (a) naming a new leaf (b) what training builds (c) the learned patterns (d) recorded spoken words
  30. 30.True or false: “old emails marked spam” is training data. (a) True (b) False
  31. 31.Which of these is a prediction? (a) past weather records (b) old emails marked spam (c) tomorrow’s rain guess (d) the trained program
  32. 32.⏩ 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
  33. 33.True or false: “the trained program” is training data. (a) True (b) False
  34. 34.True or false: “the trained program” is the model. (a) False (b) True
  35. 35.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  36. 36.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) training data (c) the model
  37. 37.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
  38. 38.⏩ 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
  39. 39.Which of these is a prediction? (a) the trained program (b) recorded spoken words (c) naming a new fruit photo (d) old emails marked spam
  40. 40.Which of these is a prediction? (a) past weather records (b) labelled fruit photos (c) old emails marked spam (d) flagging a new email

Answer key

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

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