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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 848 · 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 learned patterns”? (a) the model (b) a prediction (c) training data
  2. 2.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) a prediction (c) the model
  3. 3.📦 What happens during training? (a) it charges up (b) it takes photos (c) it learns patterns (d) it prints labels
  4. 4.⏩ 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
  5. 5.⏩ In machine learning, which comes first: recorded spoken words or the trained program? (a) recorded spoken words (b) the trained program
  6. 6.True or false: “labelled fruit photos” is a prediction. (a) False (b) True
  7. 7.🧪 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
  8. 8.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) a prediction (c) the model
  9. 9.🔮 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
  10. 10.Which of these is the model? (a) the learned patterns (b) typing a new spoken word (c) old emails marked spam (d) flagging a new email
  11. 11.⏩ In machine learning, which comes first: recorded spoken words or typing a new spoken word? (a) recorded spoken words (b) typing a new spoken word
  12. 12.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns
  13. 13.True or false: “flagging a new email” is the model. (a) False (b) True
  14. 14.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) data, model, prediction (d) model, prediction, data
  15. 15.Which of these is training data? (a) naming a new leaf (b) typing a new spoken word (c) the trained program (d) past weather records
  16. 16.True or false: “tomorrow’s rain guess” is training data. (a) True (b) False
  17. 17.Which of these is a prediction? (a) the trained program (b) old emails marked spam (c) flagging a new email (d) 500 labelled leaf photos
  18. 18.True or false: “what training builds” is a prediction. (a) True (b) False
  19. 19.Which of these is training data? (a) flagging a new email (b) the trained program (c) naming a new fruit photo (d) old emails marked spam
  20. 20.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) the model (c) training data
  21. 21.True or false: “tomorrow’s rain guess” is the model. (a) False (b) True
  22. 22.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) a prediction (b) training data (c) the model
  23. 23.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) a prediction (c) the model
  24. 24.⏩ In machine learning, which comes first: the trained program or naming a new leaf? (a) the trained program (b) naming a new leaf
  25. 25.⏩ 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
  26. 26.True or false: “what training builds” is the model. (a) False (b) True
  27. 27.True or false: “500 labelled leaf photos” is the model. (a) False (b) True
  28. 28.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  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.⏩ In machine learning, which comes first: typing a new spoken word or past weather records? (a) typing a new spoken word (b) past weather records
  31. 31.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) a prediction (c) training data
  32. 32.Which of these is the model? (a) tomorrow’s rain guess (b) labelled fruit photos (c) old emails marked spam (d) the trained program
  33. 33.Which of these is a prediction? (a) 500 labelled leaf photos (b) tomorrow’s rain guess (c) recorded spoken words (d) the trained program
  34. 34.True or false: “the trained program” is a prediction. (a) True (b) False
  35. 35.True or false: “old emails marked spam” is the model. (a) False (b) True
  36. 36.True or false: “recorded spoken words” is the model. (a) False (b) True
  37. 37.True or false: “past weather records” is training data. (a) True (b) False
  38. 38.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) the model (c) training data
  39. 39.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) training data (c) the model
  40. 40.True or false: “typing a new spoken word” is the model. (a) True (b) False

Answer key

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

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