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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 114 · 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: “500 labelled leaf photos” is a prediction. (a) False (b) True
  2. 2.Which of these is the model? (a) typing a new spoken word (b) recorded spoken words (c) the learned patterns (d) tomorrow’s rain guess
  3. 3.Which of these is a prediction? (a) old emails marked spam (b) what training builds (c) tomorrow’s rain guess (d) the trained program
  4. 4.Which of these is the model? (a) naming a new leaf (b) flagging a new email (c) what training builds (d) old emails marked spam
  5. 5.Which of these is a prediction? (a) naming a new fruit photo (b) 500 labelled leaf photos (c) labelled fruit photos (d) past weather records
  6. 6.⏩ In machine learning, which comes first: typing a new spoken word or the trained program? (a) typing a new spoken word (b) the trained program
  7. 7.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  8. 8.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  9. 9.⏩ 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
  10. 10.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) training data (c) a prediction
  11. 11.⏩ In machine learning, which comes first: tomorrow’s rain guess or the learned patterns? (a) tomorrow’s rain guess (b) the learned patterns
  12. 12.Which of these is a prediction? (a) recorded spoken words (b) past weather records (c) flagging a new email (d) the learned patterns
  13. 13.True or false: “past weather records” is training data. (a) False (b) True
  14. 14.📦 What happens during training? (a) it takes photos (b) it charges up (c) it learns patterns (d) it prints labels
  15. 15.Which of these is training data? (a) old emails marked spam (b) the trained program (c) what training builds (d) naming a new fruit photo
  16. 16.Which of these is the model? (a) naming a new fruit photo (b) tomorrow’s rain guess (c) flagging a new email (d) the trained program
  17. 17.True or false: “what training builds” is training data. (a) False (b) True
  18. 18.⏩ In machine learning, which comes first: labelled fruit photos or flagging a new email? (a) labelled fruit photos (b) flagging a new email
  19. 19.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  20. 20.⏩ In machine learning, which comes first: the trained program or naming a new leaf? (a) the trained program (b) naming a new leaf
  21. 21.🔁 What is the right order? (a) data, prediction, model (b) model, prediction, data (c) data, model, prediction (d) prediction, data, model
  22. 22.True or false: “flagging a new email” is training data. (a) False (b) True
  23. 23.⏩ In machine learning, which comes first: naming a new leaf or 500 labelled leaf photos? (a) naming a new leaf (b) 500 labelled leaf photos
  24. 24.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  25. 25.True or false: “flagging a new email” is a prediction. (a) False (b) True
  26. 26.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
  27. 27.⏩ 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
  28. 28.🧪 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
  29. 29.🔮 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
  30. 30.⏩ In machine learning, which comes first: 500 labelled leaf photos or the learned patterns? (a) 500 labelled leaf photos (b) the learned patterns
  31. 31.True or false: “the learned patterns” is the model. (a) True (b) False
  32. 32.True or false: “the learned patterns” is a prediction. (a) True (b) False
  33. 33.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  34. 34.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  35. 35.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  36. 36.⏩ In machine learning, which comes first: tomorrow’s rain guess or what training builds? (a) tomorrow’s rain guess (b) what training builds
  37. 37.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  38. 38.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) training data (c) a prediction
  39. 39.⏩ In machine learning, which comes first: the learned patterns or recorded spoken words? (a) the learned patterns (b) recorded spoken words
  40. 40.Which of these is a prediction? (a) past weather records (b) the trained program (c) old emails marked spam (d) naming a new leaf

Answer key

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

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