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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 637 · 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) training data (c) a prediction
  2. 2.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  3. 3.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program
  4. 4.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  5. 5.🧪 Why test a model on NEW examples? (a) to change its name (b) to make it slower (c) to see if it works (d) to delete its data
  6. 6.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
  7. 7.Which of these is training data? (a) typing a new spoken word (b) 500 labelled leaf photos (c) what training builds (d) naming a new fruit photo
  8. 8.True or false: “the learned patterns” is the model. (a) True (b) False
  9. 9.Which of these is training data? (a) naming a new fruit photo (b) naming a new leaf (c) old emails marked spam (d) what training builds
  10. 10.Which of these is training data? (a) naming a new leaf (b) what training builds (c) labelled fruit photos (d) the trained program
  11. 11.⏩ In machine learning, which comes first: naming a new leaf or past weather records? (a) naming a new leaf (b) past weather records
  12. 12.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  13. 13.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) training data (c) the model
  14. 14.True or false: “recorded spoken words” is the model. (a) False (b) True
  15. 15.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  16. 16.🔁 Training data, the model or a prediction: “past weather records”? (a) a prediction (b) training data (c) the model
  17. 17.⏩ In machine learning, which comes first: the learned patterns or naming a new leaf? (a) the learned patterns (b) naming a new leaf
  18. 18.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  19. 19.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, prediction, model (d) data, model, prediction
  20. 20.True or false: “past weather records” is training data. (a) False (b) True
  21. 21.⏩ In machine learning, which comes first: naming a new fruit photo or the trained program? (a) naming a new fruit photo (b) the trained program
  22. 22.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  23. 23.True or false: “flagging a new email” is the model. (a) False (b) True
  24. 24.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  25. 25.True or false: “the learned patterns” is training data. (a) True (b) False
  26. 26.Which of these is a prediction? (a) old emails marked spam (b) recorded spoken words (c) typing a new spoken word (d) what training builds
  27. 27.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  28. 28.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) a prediction (c) a label error (d) training data
  29. 29.True or false: “the trained program” is training data. (a) True (b) False
  30. 30.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  31. 31.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  32. 32.Which of these is a prediction? (a) the trained program (b) labelled fruit photos (c) flagging a new email (d) recorded spoken words
  33. 33.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  34. 34.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) a prediction (c) the model
  35. 35.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) a prediction (c) the model
  36. 36.⏩ In machine learning, which comes first: naming a new fruit photo or what training builds? (a) naming a new fruit photo (b) what training builds
  37. 37.True or false: “old emails marked spam” is the model. (a) False (b) True
  38. 38.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  39. 39.True or false: “tomorrow’s rain guess” is training data. (a) True (b) False
  40. 40.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) training data (c) a prediction

Answer key

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

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