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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 204 · 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) the model (b) training data (c) a prediction
  2. 2.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) training data (c) the model
  3. 3.⏩ In machine learning, which comes first: naming a new fruit photo or old emails marked spam? (a) naming a new fruit photo (b) old emails marked spam
  4. 4.True or false: “typing a new spoken word” is the model. (a) False (b) True
  5. 5.Which of these is training data? (a) tomorrow’s rain guess (b) past weather records (c) the learned patterns (d) what training builds
  6. 6.Which of these is a prediction? (a) 500 labelled leaf photos (b) recorded spoken words (c) naming a new fruit photo (d) old emails marked spam
  7. 7.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) a prediction (c) training data
  8. 8.🧪 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
  9. 9.True or false: “labelled fruit photos” is training data. (a) False (b) True
  10. 10.True or false: “naming a new leaf” is training data. (a) False (b) True
  11. 11.Which of these is training data? (a) the learned patterns (b) tomorrow’s rain guess (c) labelled fruit photos (d) naming a new leaf
  12. 12.Which of these is the model? (a) old emails marked spam (b) the learned patterns (c) labelled fruit photos (d) flagging a new email
  13. 13.Which of these is a prediction? (a) labelled fruit photos (b) flagging a new email (c) old emails marked spam (d) 500 labelled leaf photos
  14. 14.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it charges up (d) it learns patterns
  15. 15.True or false: “naming a new leaf” is a prediction. (a) True (b) False
  16. 16.Which of these is training data? (a) old emails marked spam (b) the trained program (c) flagging a new email (d) naming a new fruit photo
  17. 17.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a prediction (c) a label error (d) a password
  18. 18.⏩ 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
  19. 19.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  20. 20.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  21. 21.Which of these is a prediction? (a) recorded spoken words (b) the learned patterns (c) typing a new spoken word (d) past weather records
  22. 22.True or false: “naming a new leaf” is the model. (a) True (b) False
  23. 23.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
  24. 24.True or false: “recorded spoken words” is a prediction. (a) True (b) False
  25. 25.True or false: “what training builds” is a prediction. (a) True (b) False
  26. 26.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  27. 27.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) a prediction (c) training data
  28. 28.⏩ In machine learning, which comes first: the learned patterns or naming a new leaf? (a) the learned patterns (b) naming a new leaf
  29. 29.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  30. 30.True or false: “the learned patterns” is training data. (a) True (b) False
  31. 31.🔁 Training data, the model or a prediction: “the trained program”? (a) the model (b) a prediction (c) training data
  32. 32.⏩ In machine learning, which comes first: past weather records or flagging a new email? (a) past weather records (b) flagging a new email
  33. 33.True or false: “the trained program” is training data. (a) False (b) True
  34. 34.⏩ In machine learning, which comes first: what training builds or tomorrow’s rain guess? (a) what training builds (b) tomorrow’s rain guess
  35. 35.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  36. 36.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  37. 37.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  38. 38.Which of these is training data? (a) flagging a new email (b) what training builds (c) the learned patterns (d) 500 labelled leaf photos
  39. 39.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  40. 40.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) a prediction (b) the model (c) training data

Answer key

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

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