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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 388 · 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.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a label error (c) a password (d) a prediction
  2. 2.Which of these is training data? (a) what training builds (b) the trained program (c) 500 labelled leaf photos (d) naming a new leaf
  3. 3.True or false: “tomorrow’s rain guess” is training data. (a) False (b) True
  4. 4.Which of these is a prediction? (a) recorded spoken words (b) what training builds (c) naming a new fruit photo (d) past weather records
  5. 5.Which of these is training data? (a) what training builds (b) flagging a new email (c) past weather records (d) naming a new fruit photo
  6. 6.⏩ In machine learning, which comes first: the trained program or naming a new leaf? (a) the trained program (b) naming a new leaf
  7. 7.⏩ In machine learning, which comes first: the trained program or 500 labelled leaf photos? (a) the trained program (b) 500 labelled leaf photos
  8. 8.⏩ In machine learning, which comes first: 500 labelled leaf photos or flagging a new email? (a) 500 labelled leaf photos (b) flagging a new email
  9. 9.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  10. 10.True or false: “naming a new leaf” is training data. (a) False (b) True
  11. 11.Which of these is a prediction? (a) 500 labelled leaf photos (b) flagging a new email (c) past weather records (d) the learned patterns
  12. 12.True or false: “the learned patterns” is training data. (a) False (b) True
  13. 13.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) training data (c) a prediction
  14. 14.⏩ In machine learning, which comes first: the trained program or labelled fruit photos? (a) the trained program (b) labelled fruit photos
  15. 15.Which of these is a prediction? (a) old emails marked spam (b) typing a new spoken word (c) the learned patterns (d) the trained program
  16. 16.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
  17. 17.📦 What happens during training? (a) it charges up (b) it prints labels (c) it takes photos (d) it learns patterns
  18. 18.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  19. 19.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  20. 20.Which of these is training data? (a) naming a new leaf (b) labelled fruit photos (c) typing a new spoken word (d) tomorrow’s rain guess
  21. 21.True or false: “what training builds” is training data. (a) False (b) True
  22. 22.⏩ In machine learning, which comes first: what training builds or tomorrow’s rain guess? (a) what training builds (b) tomorrow’s rain guess
  23. 23.Which of these is a prediction? (a) what training builds (b) naming a new leaf (c) the trained program (d) old emails marked spam
  24. 24.🔁 What is the right order? (a) data, prediction, model (b) prediction, data, model (c) model, prediction, data (d) data, model, prediction
  25. 25.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  26. 26.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  27. 27.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) training data (c) the model
  28. 28.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) a prediction (c) training data
  29. 29.True or false: “typing a new spoken word” is the model. (a) False (b) True
  30. 30.Which of these is training data? (a) naming a new leaf (b) the learned patterns (c) old emails marked spam (d) the trained program
  31. 31.True or false: “old emails marked spam” is a prediction. (a) True (b) False
  32. 32.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) training data (c) the model
  33. 33.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  34. 34.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  35. 35.True or false: “the learned patterns” is the model. (a) False (b) True
  36. 36.True or false: “old emails marked spam” is training data. (a) False (b) True
  37. 37.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
  38. 38.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  39. 39.⏩ 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
  40. 40.⏩ In machine learning, which comes first: the learned patterns or naming a new fruit photo? (a) the learned patterns (b) naming a new fruit photo

Answer key

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

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