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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 374 · 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.True or false: “naming a new leaf” is a prediction. (a) True (b) False
  2. 2.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) training data (d) a password
  3. 3.Which of these is training data? (a) past weather records (b) the trained program (c) typing a new spoken word (d) the learned patterns
  4. 4.True or false: “typing a new spoken word” is a prediction. (a) False (b) True
  5. 5.Which of these is the model? (a) flagging a new email (b) the trained program (c) old emails marked spam (d) labelled fruit photos
  6. 6.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) a prediction (c) the model
  7. 7.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) model, prediction, data (d) data, model, prediction
  8. 8.📦 What happens during training? (a) it prints labels (b) it learns patterns (c) it takes photos (d) it charges up
  9. 9.Which of these is the model? (a) flagging a new email (b) 500 labelled leaf photos (c) old emails marked spam (d) what training builds
  10. 10.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) training data (c) a prediction
  11. 11.Which of these is training data? (a) old emails marked spam (b) the learned patterns (c) typing a new spoken word (d) the trained program
  12. 12.🧪 Why test a model on NEW examples? (a) to see if it works (b) to delete its data (c) to change its name (d) to make it slower
  13. 13.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  14. 14.Which of these is the model? (a) recorded spoken words (b) typing a new spoken word (c) the learned patterns (d) tomorrow’s rain guess
  15. 15.True or false: “recorded spoken words” is training data. (a) False (b) True
  16. 16.⏩ 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
  17. 17.True or false: “recorded spoken words” is the model. (a) True (b) False
  18. 18.Which of these is training data? (a) labelled fruit photos (b) naming a new fruit photo (c) the trained program (d) typing a new spoken word
  19. 19.True or false: “what training builds” is training data. (a) True (b) False
  20. 20.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  21. 21.True or false: “old emails marked spam” is training data. (a) False (b) True
  22. 22.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  23. 23.⏩ In machine learning, which comes first: the trained program or naming a new leaf? (a) the trained program (b) naming a new leaf
  24. 24.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  25. 25.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
  26. 26.⏩ In machine learning, which comes first: flagging a new email or 500 labelled leaf photos? (a) flagging a new email (b) 500 labelled leaf photos
  27. 27.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  28. 28.⏩ In machine learning, which comes first: flagging a new email or old emails marked spam? (a) flagging a new email (b) old emails marked spam
  29. 29.⏩ In machine learning, which comes first: what training builds or typing a new spoken word? (a) what training builds (b) typing a new spoken word
  30. 30.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  31. 31.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) a prediction (c) training data
  32. 32.Which of these is a prediction? (a) the learned patterns (b) tomorrow’s rain guess (c) recorded spoken words (d) labelled fruit photos
  33. 33.True or false: “the learned patterns” is the model. (a) False (b) True
  34. 34.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) a prediction (c) the model
  35. 35.Which of these is training data? (a) 500 labelled leaf photos (b) the trained program (c) flagging a new email (d) what training builds
  36. 36.Which of these is a prediction? (a) naming a new fruit photo (b) recorded spoken words (c) what training builds (d) the trained program
  37. 37.True or false: “past weather records” is training data. (a) False (b) True
  38. 38.⏩ In machine learning, which comes first: the trained program or naming a new fruit photo? (a) the trained program (b) naming a new fruit photo
  39. 39.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  40. 40.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data

Answer key

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

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