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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 901 · 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 password (c) a label error (d) a prediction
  2. 2.True or false: “what training builds” is the model. (a) False (b) True
  3. 3.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) the model (c) training data
  4. 4.True or false: “labelled fruit photos” is a prediction. (a) True (b) False
  5. 5.🔁 Training data, the model or a prediction: “the learned patterns”? (a) a prediction (b) the model (c) training data
  6. 6.Which of these is the model? (a) naming a new leaf (b) 500 labelled leaf photos (c) the trained program (d) labelled fruit photos
  7. 7.⏩ In machine learning, which comes first: flagging a new email or the learned patterns? (a) flagging a new email (b) the learned patterns
  8. 8.⏩ In machine learning, which comes first: tomorrow’s rain guess or recorded spoken words? (a) tomorrow’s rain guess (b) recorded spoken words
  9. 9.🧪 Why test a model on NEW examples? (a) to delete its data (b) to see if it works (c) to make it slower (d) to change its name
  10. 10.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) the model (c) a prediction
  11. 11.True or false: “recorded spoken words” is the model. (a) True (b) False
  12. 12.⏩ In machine learning, which comes first: old emails marked spam or the learned patterns? (a) old emails marked spam (b) the learned patterns
  13. 13.Which of these is a prediction? (a) the trained program (b) recorded spoken words (c) labelled fruit photos (d) typing a new spoken word
  14. 14.⏩ In machine learning, which comes first: the trained program or recorded spoken words? (a) the trained program (b) recorded spoken words
  15. 15.⏩ 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
  16. 16.True or false: “recorded spoken words” is a prediction. (a) True (b) False
  17. 17.Which of these is training data? (a) the learned patterns (b) what training builds (c) the trained program (d) old emails marked spam
  18. 18.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, model, prediction (d) data, prediction, model
  19. 19.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) the model (c) a prediction
  20. 20.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  21. 21.🔁 Training data, the model or a prediction: “what training builds”? (a) the model (b) training data (c) a prediction
  22. 22.True or false: “past weather records” is the model. (a) False (b) True
  23. 23.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
  24. 24.⏩ In machine learning, which comes first: flagging a new email or what training builds? (a) flagging a new email (b) what training builds
  25. 25.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) the model (c) training data
  26. 26.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
  27. 27.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  28. 28.⏩ In machine learning, which comes first: the learned patterns or recorded spoken words? (a) the learned patterns (b) recorded spoken words
  29. 29.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it charges up (d) it prints labels
  30. 30.Which of these is training data? (a) the trained program (b) flagging a new email (c) labelled fruit photos (d) typing a new spoken word
  31. 31.Which of these is the model? (a) the learned patterns (b) flagging a new email (c) naming a new leaf (d) past weather records
  32. 32.True or false: “labelled fruit photos” is training data. (a) False (b) True
  33. 33.True or false: “recorded spoken words” is training data. (a) True (b) False
  34. 34.True or false: “the trained program” is the model. (a) True (b) False
  35. 35.⏩ In machine learning, which comes first: naming a new leaf or 500 labelled leaf photos? (a) naming a new leaf (b) 500 labelled leaf photos
  36. 36.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) the model (c) a prediction
  37. 37.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  38. 38.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  39. 39.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  40. 40.True or false: “what training builds” is training data. (a) False (b) True

Answer key

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

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