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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 158 · 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.Which of these is the model? (a) naming a new leaf (b) flagging a new email (c) typing a new spoken word (d) the learned patterns
  2. 2.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) training data (c) a prediction
  3. 3.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a label error (c) a password (d) training data
  4. 4.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  5. 5.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) the model (c) training data
  6. 6.True or false: “old emails marked spam” is the model. (a) True (b) False
  7. 7.Which of these is training data? (a) the learned patterns (b) typing a new spoken word (c) old emails marked spam (d) tomorrow’s rain guess
  8. 8.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  9. 9.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  10. 10.📦 What happens during training? (a) it takes photos (b) it prints labels (c) it learns patterns (d) it charges up
  11. 11.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  12. 12.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  13. 13.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  14. 14.Which of these is the model? (a) old emails marked spam (b) flagging a new email (c) typing a new spoken word (d) what training builds
  15. 15.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  16. 16.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  17. 17.True or false: “naming a new leaf” is the model. (a) False (b) True
  18. 18.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  19. 19.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  20. 20.⏩ In machine learning, which comes first: flagging a new email or what training builds? (a) flagging a new email (b) what training builds
  21. 21.Which of these is training data? (a) typing a new spoken word (b) the learned patterns (c) recorded spoken words (d) what training builds
  22. 22.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
  23. 23.True or false: “old emails marked spam” is training data. (a) True (b) False
  24. 24.⏩ In machine learning, which comes first: naming a new fruit photo or the trained program? (a) naming a new fruit photo (b) the trained program
  25. 25.True or false: “the learned patterns” is a prediction. (a) False (b) True
  26. 26.⏩ In machine learning, which comes first: recorded spoken words or what training builds? (a) recorded spoken words (b) what training builds
  27. 27.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
  28. 28.⏩ In machine learning, which comes first: past weather records or what training builds? (a) past weather records (b) what training builds
  29. 29.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) the model (c) training data
  30. 30.True or false: “recorded spoken words” is a prediction. (a) True (b) False
  31. 31.Which of these is a prediction? (a) what training builds (b) the learned patterns (c) old emails marked spam (d) naming a new leaf
  32. 32.⏩ In machine learning, which comes first: past weather records or tomorrow’s rain guess? (a) past weather records (b) tomorrow’s rain guess
  33. 33.⏩ In machine learning, which comes first: the trained program or naming a new fruit photo? (a) the trained program (b) naming a new fruit photo
  34. 34.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) data, model, prediction (d) model, prediction, data
  35. 35.Which of these is training data? (a) labelled fruit photos (b) naming a new leaf (c) tomorrow’s rain guess (d) the trained program
  36. 36.True or false: “past weather records” is a prediction. (a) False (b) True
  37. 37.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program
  38. 38.True or false: “labelled fruit photos” is training data. (a) False (b) True
  39. 39.⏩ In machine learning, which comes first: past weather records or the learned patterns? (a) past weather records (b) the learned patterns
  40. 40.🧪 Why test a model on NEW examples? (a) to delete its data (b) to make it slower (c) to see if it works (d) to change its name

Answer key

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

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