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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 853 · 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: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  2. 2.Which of these is a prediction? (a) the trained program (b) 500 labelled leaf photos (c) typing a new spoken word (d) recorded spoken words
  3. 3.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) training data (c) the model
  4. 4.Which of these is a prediction? (a) the trained program (b) past weather records (c) naming a new fruit photo (d) the learned patterns
  5. 5.True or false: “the learned patterns” is training data. (a) True (b) False
  6. 6.Which of these is a prediction? (a) what training builds (b) naming a new leaf (c) 500 labelled leaf photos (d) recorded spoken words
  7. 7.🧪 Why test a model on NEW examples? (a) to change its name (b) to see if it works (c) to delete its data (d) to make it slower
  8. 8.Which of these is the model? (a) old emails marked spam (b) the trained program (c) naming a new leaf (d) flagging a new email
  9. 9.⏩ In machine learning, which comes first: the trained program or past weather records? (a) the trained program (b) past weather records
  10. 10.⏩ In machine learning, which comes first: recorded spoken words or flagging a new email? (a) recorded spoken words (b) flagging a new email
  11. 11.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  12. 12.Which of these is training data? (a) naming a new fruit photo (b) the learned patterns (c) recorded spoken words (d) flagging a new email
  13. 13.True or false: “recorded spoken words” is the model. (a) False (b) True
  14. 14.True or false: “tomorrow’s rain guess” is the model. (a) True (b) False
  15. 15.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) a prediction (c) training data
  16. 16.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  17. 17.True or false: “naming a new leaf” is training data. (a) True (b) False
  18. 18.Which of these is training data? (a) the trained program (b) 500 labelled leaf photos (c) what training builds (d) flagging a new email
  19. 19.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it charges up (d) it prints labels
  20. 20.Which of these is the model? (a) the learned patterns (b) typing a new spoken word (c) naming a new leaf (d) old emails marked spam
  21. 21.True or false: “500 labelled leaf photos” is a prediction. (a) True (b) False
  22. 22.🔮 A trained model sees a new photo and says “mango”. This is… (a) a prediction (b) a password (c) training data (d) a label error
  23. 23.⏩ In machine learning, which comes first: flagging a new email or recorded spoken words? (a) flagging a new email (b) recorded spoken words
  24. 24.Which of these is the model? (a) labelled fruit photos (b) what training builds (c) naming a new leaf (d) recorded spoken words
  25. 25.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program
  26. 26.Which of these is a prediction? (a) past weather records (b) what training builds (c) tomorrow’s rain guess (d) old emails marked spam
  27. 27.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) training data (c) the model
  28. 28.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  29. 29.True or false: “recorded spoken words” is training data. (a) False (b) True
  30. 30.True or false: “the learned patterns” is a prediction. (a) False (b) True
  31. 31.True or false: “tomorrow’s rain guess” is training data. (a) True (b) False
  32. 32.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  33. 33.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, prediction, model (d) data, model, prediction
  34. 34.⏩ 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
  35. 35.⏩ In machine learning, which comes first: past weather records or naming a new leaf? (a) past weather records (b) naming a new leaf
  36. 36.Which of these is training data? (a) typing a new spoken word (b) the trained program (c) naming a new leaf (d) old emails marked spam
  37. 37.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data
  38. 38.True or false: “flagging a new email” is the model. (a) True (b) False
  39. 39.Which of these is training data? (a) the trained program (b) what training builds (c) labelled fruit photos (d) the learned patterns
  40. 40.True or false: “old emails marked spam” is training data. (a) False (b) True

Answer key

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

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