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AI Worksheet: Training Data, Model, Prediction

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

Answer key

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

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