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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 244 · 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: “what training builds”? (a) training data (b) a prediction (c) the model
  2. 2.True or false: “the learned patterns” is a prediction. (a) False (b) True
  3. 3.Which of these is a prediction? (a) labelled fruit photos (b) naming a new leaf (c) 500 labelled leaf photos (d) recorded spoken words
  4. 4.Which of these is a prediction? (a) past weather records (b) the learned patterns (c) tomorrow’s rain guess (d) 500 labelled leaf photos
  5. 5.⏩ In machine learning, which comes first: the trained program or tomorrow’s rain guess? (a) the trained program (b) tomorrow’s rain guess
  6. 6.Which of these is training data? (a) typing a new spoken word (b) what training builds (c) labelled fruit photos (d) the trained program
  7. 7.Which of these is training data? (a) the learned patterns (b) what training builds (c) past weather records (d) flagging a new email
  8. 8.True or false: “typing a new spoken word” is training data. (a) True (b) False
  9. 9.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) the model (c) a prediction
  10. 10.Which of these is the model? (a) flagging a new email (b) tomorrow’s rain guess (c) typing a new spoken word (d) the learned patterns
  11. 11.True or false: “past weather records” is training data. (a) True (b) False
  12. 12.True or false: “labelled fruit photos” is the model. (a) True (b) False
  13. 13.⏩ In machine learning, which comes first: the trained program or labelled fruit photos? (a) the trained program (b) labelled fruit photos
  14. 14.True or false: “500 labelled leaf photos” is a prediction. (a) False (b) True
  15. 15.True or false: “recorded spoken words” is the model. (a) True (b) False
  16. 16.🧪 Why test a model on NEW examples? (a) to see if it works (b) to make it slower (c) to delete its data (d) to change its name
  17. 17.⏩ 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
  18. 18.⏩ In machine learning, which comes first: old emails marked spam or what training builds? (a) old emails marked spam (b) what training builds
  19. 19.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  20. 20.True or false: “the learned patterns” is training data. (a) True (b) False
  21. 21.Which of these is training data? (a) old emails marked spam (b) naming a new leaf (c) the trained program (d) flagging a new email
  22. 22.⏩ In machine learning, which comes first: the trained program or old emails marked spam? (a) the trained program (b) old emails marked spam
  23. 23.True or false: “500 labelled leaf photos” is training data. (a) False (b) True
  24. 24.Which of these is a prediction? (a) flagging a new email (b) recorded spoken words (c) 500 labelled leaf photos (d) past weather records
  25. 25.Which of these is training data? (a) 500 labelled leaf photos (b) naming a new fruit photo (c) the trained program (d) tomorrow’s rain guess
  26. 26.🔁 What is the right order? (a) model, prediction, data (b) data, model, prediction (c) data, prediction, model (d) prediction, data, model
  27. 27.Which of these is the model? (a) recorded spoken words (b) tomorrow’s rain guess (c) 500 labelled leaf photos (d) the trained program
  28. 28.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) training data (b) the model (c) a prediction
  29. 29.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) the model (c) a prediction
  30. 30.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  31. 31.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a prediction (c) a password (d) training data
  32. 32.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  33. 33.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) the model (c) a prediction
  34. 34.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  35. 35.Which of these is a prediction? (a) the learned patterns (b) 500 labelled leaf photos (c) past weather records (d) naming a new fruit photo
  36. 36.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) the model (c) a prediction
  37. 37.True or false: “what training builds” is training data. (a) True (b) False
  38. 38.True or false: “the learned patterns” is the model. (a) True (b) False
  39. 39.True or false: “past weather records” is a prediction. (a) True (b) False
  40. 40.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model

Answer key

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

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