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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 372 · 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.🧪 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
  2. 2.Which of these is a prediction? (a) flagging a new email (b) labelled fruit photos (c) 500 labelled leaf photos (d) old emails marked spam
  3. 3.📦 What happens during training? (a) it takes photos (b) it prints labels (c) it learns patterns (d) it charges up
  4. 4.Which of these is training data? (a) labelled fruit photos (b) naming a new fruit photo (c) naming a new leaf (d) typing a new spoken word
  5. 5.🔁 What is the right order? (a) prediction, data, model (b) data, model, prediction (c) model, prediction, data (d) data, prediction, model
  6. 6.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) the model (c) training data
  7. 7.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  8. 8.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) the model (b) training data (c) a prediction
  9. 9.True or false: “flagging a new email” is a prediction. (a) False (b) True
  10. 10.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) training data (c) the model
  11. 11.Which of these is training data? (a) tomorrow’s rain guess (b) what training builds (c) old emails marked spam (d) naming a new leaf
  12. 12.⏩ In machine learning, which comes first: the learned patterns or typing a new spoken word? (a) the learned patterns (b) typing a new spoken word
  13. 13.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) a prediction (b) training data (c) the model
  14. 14.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  15. 15.True or false: “labelled fruit photos” is training data. (a) False (b) True
  16. 16.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
  17. 17.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  18. 18.Which of these is the model? (a) naming a new fruit photo (b) the learned patterns (c) tomorrow’s rain guess (d) labelled fruit photos
  19. 19.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) training data (c) a label error (d) a prediction
  20. 20.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  21. 21.⏩ 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
  22. 22.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  23. 23.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  24. 24.True or false: “the learned patterns” is training data. (a) True (b) False
  25. 25.Which of these is a prediction? (a) tomorrow’s rain guess (b) old emails marked spam (c) 500 labelled leaf photos (d) the learned patterns
  26. 26.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  27. 27.Which of these is training data? (a) naming a new leaf (b) the trained program (c) 500 labelled leaf photos (d) typing a new spoken word
  28. 28.True or false: “recorded spoken words” is the model. (a) True (b) False
  29. 29.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  30. 30.⏩ 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
  31. 31.Which of these is training data? (a) naming a new fruit photo (b) the trained program (c) recorded spoken words (d) tomorrow’s rain guess
  32. 32.Which of these is a prediction? (a) naming a new leaf (b) what training builds (c) past weather records (d) 500 labelled leaf photos
  33. 33.True or false: “old emails marked spam” is a prediction. (a) True (b) False
  34. 34.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) the model (c) a prediction
  35. 35.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
  36. 36.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
  37. 37.True or false: “the learned patterns” is a prediction. (a) True (b) False
  38. 38.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
  39. 39.True or false: “500 labelled leaf photos” is training data. (a) True (b) False
  40. 40.True or false: “what training builds” is the model. (a) False (b) True

Answer key

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

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