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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 446 · 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.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  2. 2.📦 What happens during training? (a) it learns patterns (b) it takes photos (c) it prints labels (d) it charges up
  3. 3.⏩ In machine learning, which comes first: past weather records or the learned patterns? (a) past weather records (b) the learned patterns
  4. 4.Which of these is the model? (a) the trained program (b) naming a new fruit photo (c) recorded spoken words (d) old emails marked spam
  5. 5.Which of these is training data? (a) old emails marked spam (b) the trained program (c) the learned patterns (d) naming a new fruit photo
  6. 6.Which of these is a prediction? (a) the learned patterns (b) 500 labelled leaf photos (c) past weather records (d) typing a new spoken word
  7. 7.True or false: “naming a new leaf” is training data. (a) True (b) False
  8. 8.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  9. 9.True or false: “tomorrow’s rain guess” is training data. (a) True (b) False
  10. 10.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  11. 11.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) training data (c) the model
  12. 12.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) the model (c) training data
  13. 13.Which of these is training data? (a) naming a new fruit photo (b) recorded spoken words (c) the learned patterns (d) naming a new leaf
  14. 14.🔁 What is the right order? (a) prediction, data, model (b) data, model, prediction (c) data, prediction, model (d) model, prediction, data
  15. 15.🔮 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
  16. 16.Which of these is a prediction? (a) naming a new leaf (b) recorded spoken words (c) 500 labelled leaf photos (d) old emails marked spam
  17. 17.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  18. 18.True or false: “recorded spoken words” is training data. (a) False (b) True
  19. 19.True or false: “the trained program” is the model. (a) True (b) False
  20. 20.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  21. 21.Which of these is a prediction? (a) the learned patterns (b) what training builds (c) recorded spoken words (d) naming a new fruit photo
  22. 22.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
  23. 23.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) a prediction (c) the model
  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.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) the model (c) training data
  26. 26.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  27. 27.True or false: “old emails marked spam” is the model. (a) False (b) True
  28. 28.⏩ In machine learning, which comes first: typing a new spoken word or what training builds? (a) typing a new spoken word (b) what training builds
  29. 29.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  30. 30.Which of these is training data? (a) naming a new fruit photo (b) labelled fruit photos (c) what training builds (d) flagging a new email
  31. 31.🧪 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
  32. 32.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  33. 33.True or false: “labelled fruit photos” is training data. (a) True (b) False
  34. 34.True or false: “the learned patterns” is a prediction. (a) True (b) False
  35. 35.Which of these is training data? (a) 500 labelled leaf photos (b) the trained program (c) naming a new fruit photo (d) typing a new spoken word
  36. 36.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  37. 37.Which of these is a prediction? (a) flagging a new email (b) recorded spoken words (c) the learned patterns (d) what training builds
  38. 38.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  39. 39.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  40. 40.Which of these is training data? (a) past weather records (b) naming a new leaf (c) tomorrow’s rain guess (d) typing a new spoken word

Answer key

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

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