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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 241 · 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.📦 What happens during training? (a) it charges up (b) it learns patterns (c) it prints labels (d) it takes photos
  2. 2.🧪 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
  3. 3.⏩ In machine learning, which comes first: the trained program or flagging a new email? (a) the trained program (b) flagging a new email
  4. 4.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
  5. 5.⏩ In machine learning, which comes first: tomorrow’s rain guess or the trained program? (a) tomorrow’s rain guess (b) the trained program
  6. 6.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a prediction (c) a password (d) a label error
  7. 7.Which of these is a prediction? (a) naming a new leaf (b) old emails marked spam (c) 500 labelled leaf photos (d) the trained program
  8. 8.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  9. 9.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  10. 10.Which of these is the model? (a) 500 labelled leaf photos (b) the trained program (c) old emails marked spam (d) tomorrow’s rain guess
  11. 11.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) training data (c) a prediction
  12. 12.True or false: “labelled fruit photos” is the model. (a) True (b) False
  13. 13.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) a prediction (c) the model
  14. 14.Which of these is a prediction? (a) tomorrow’s rain guess (b) recorded spoken words (c) 500 labelled leaf photos (d) old emails marked spam
  15. 15.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  16. 16.Which of these is training data? (a) the learned patterns (b) typing a new spoken word (c) past weather records (d) naming a new fruit photo
  17. 17.Which of these is training data? (a) the trained program (b) recorded spoken words (c) typing a new spoken word (d) what training builds
  18. 18.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  19. 19.True or false: “recorded spoken words” is a prediction. (a) True (b) False
  20. 20.True or false: “old emails marked spam” is training data. (a) False (b) True
  21. 21.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) the model (c) a prediction
  22. 22.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  23. 23.True or false: “past weather records” is training data. (a) True (b) False
  24. 24.Which of these is training data? (a) typing a new spoken word (b) the trained program (c) 500 labelled leaf photos (d) tomorrow’s rain guess
  25. 25.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  26. 26.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  27. 27.⏩ In machine learning, which comes first: the learned patterns or 500 labelled leaf photos? (a) the learned patterns (b) 500 labelled leaf photos
  28. 28.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) the model (c) training data
  29. 29.True or false: “what training builds” is the model. (a) False (b) True
  30. 30.Which of these is training data? (a) typing a new spoken word (b) the learned patterns (c) old emails marked spam (d) the trained program
  31. 31.True or false: “the learned patterns” is the model. (a) True (b) False
  32. 32.⏩ 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
  33. 33.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  34. 34.True or false: “labelled fruit photos” is training data. (a) False (b) True
  35. 35.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) the model (c) training data
  36. 36.True or false: “flagging a new email” is a prediction. (a) False (b) True
  37. 37.⏩ 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
  38. 38.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, prediction, model (d) data, model, prediction
  39. 39.Which of these is a prediction? (a) old emails marked spam (b) flagging a new email (c) past weather records (d) the trained program
  40. 40.True or false: “500 labelled leaf photos” is the model. (a) True (b) False

Answer key

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

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