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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 710 · 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: “the trained program”? (a) the model (b) a prediction (c) training data
  2. 2.Which of these is the model? (a) old emails marked spam (b) the learned patterns (c) recorded spoken words (d) 500 labelled leaf photos
  3. 3.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) a prediction (c) the model
  4. 4.True or false: “past weather records” is training data. (a) True (b) False
  5. 5.⏩ In machine learning, which comes first: past weather records or naming a new leaf? (a) past weather records (b) naming a new leaf
  6. 6.🔁 What is the right order? (a) data, model, prediction (b) data, prediction, model (c) model, prediction, data (d) prediction, data, model
  7. 7.True or false: “old emails marked spam” is training data. (a) False (b) True
  8. 8.⏩ In machine learning, which comes first: recorded spoken words or naming a new leaf? (a) recorded spoken words (b) naming a new leaf
  9. 9.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) training data (c) a prediction
  10. 10.⏩ In machine learning, which comes first: typing a new spoken word or the learned patterns? (a) typing a new spoken word (b) the learned patterns
  11. 11.Which of these is the model? (a) typing a new spoken word (b) flagging a new email (c) tomorrow’s rain guess (d) the trained program
  12. 12.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
  13. 13.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) training data (c) a prediction
  14. 14.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  15. 15.True or false: “recorded spoken words” is training data. (a) True (b) False
  16. 16.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) a prediction (c) the model
  17. 17.True or false: “recorded spoken words” is the model. (a) False (b) True
  18. 18.🧪 Why test a model on NEW examples? (a) to make it slower (b) to see if it works (c) to change its name (d) to delete its data
  19. 19.Which of these is a prediction? (a) recorded spoken words (b) flagging a new email (c) what training builds (d) the trained program
  20. 20.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  21. 21.⏩ In machine learning, which comes first: flagging a new email or past weather records? (a) flagging a new email (b) past weather records
  22. 22.Which of these is the model? (a) labelled fruit photos (b) what training builds (c) 500 labelled leaf photos (d) naming a new fruit photo
  23. 23.True or false: “past weather records” is a prediction. (a) False (b) True
  24. 24.True or false: “naming a new leaf” is training data. (a) True (b) False
  25. 25.⏩ 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
  26. 26.⏩ In machine learning, which comes first: past weather records or the trained program? (a) past weather records (b) the trained program
  27. 27.⏩ In machine learning, which comes first: the trained program or 500 labelled leaf photos? (a) the trained program (b) 500 labelled leaf photos
  28. 28.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
  29. 29.Which of these is a prediction? (a) past weather records (b) the learned patterns (c) what training builds (d) naming a new fruit photo
  30. 30.Which of these is training data? (a) typing a new spoken word (b) old emails marked spam (c) naming a new fruit photo (d) tomorrow’s rain guess
  31. 31.Which of these is a prediction? (a) labelled fruit photos (b) what training builds (c) old emails marked spam (d) tomorrow’s rain guess
  32. 32.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  33. 33.True or false: “flagging a new email” is a prediction. (a) True (b) False
  34. 34.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
  35. 35.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  36. 36.Which of these is training data? (a) what training builds (b) the trained program (c) 500 labelled leaf photos (d) typing a new spoken word
  37. 37.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  38. 38.Which of these is training data? (a) flagging a new email (b) tomorrow’s rain guess (c) recorded spoken words (d) naming a new fruit photo
  39. 39.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  40. 40.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) a prediction (c) training data

Answer key

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

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