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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 674 · 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.⏩ 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
  3. 3.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) the model (c) a prediction
  4. 4.Which of these is the model? (a) tomorrow’s rain guess (b) what training builds (c) labelled fruit photos (d) recorded spoken words
  5. 5.True or false: “tomorrow’s rain guess” is training data. (a) True (b) False
  6. 6.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  7. 7.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  8. 8.📦 What happens during training? (a) it takes photos (b) it learns patterns (c) it prints labels (d) it charges up
  9. 9.Which of these is the model? (a) past weather records (b) typing a new spoken word (c) tomorrow’s rain guess (d) the learned patterns
  10. 10.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) a prediction (c) the model
  11. 11.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, model, prediction (d) data, prediction, model
  12. 12.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) training data (c) the model
  13. 13.Which of these is training data? (a) recorded spoken words (b) what training builds (c) typing a new spoken word (d) the learned patterns
  14. 14.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) a prediction (c) training data
  15. 15.Which of these is a prediction? (a) what training builds (b) labelled fruit photos (c) old emails marked spam (d) tomorrow’s rain guess
  16. 16.True or false: “naming a new leaf” is a prediction. (a) False (b) True
  17. 17.Which of these is the model? (a) tomorrow’s rain guess (b) old emails marked spam (c) flagging a new email (d) the trained program
  18. 18.True or false: “labelled fruit photos” is the model. (a) False (b) True
  19. 19.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  20. 20.⏩ In machine learning, which comes first: tomorrow’s rain guess or the learned patterns? (a) tomorrow’s rain guess (b) the learned patterns
  21. 21.True or false: “labelled fruit photos” is training data. (a) True (b) False
  22. 22.True or false: “old emails marked spam” is training data. (a) False (b) True
  23. 23.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) a prediction (c) the model
  24. 24.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) a password (c) training data (d) a prediction
  25. 25.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) the model (c) training data
  26. 26.True or false: “recorded spoken words” is training data. (a) False (b) True
  27. 27.🧪 Why test a model on NEW examples? (a) to change its name (b) to see if it works (c) to make it slower (d) to delete its data
  28. 28.⏩ In machine learning, which comes first: past weather records or naming a new fruit photo? (a) past weather records (b) naming a new fruit photo
  29. 29.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  30. 30.Which of these is training data? (a) what training builds (b) past weather records (c) tomorrow’s rain guess (d) flagging a new email
  31. 31.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  32. 32.True or false: “what training builds” is a prediction. (a) False (b) True
  33. 33.⏩ 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
  34. 34.True or false: “what training builds” is the model. (a) True (b) False
  35. 35.True or false: “past weather records” is a prediction. (a) True (b) False
  36. 36.Which of these is a prediction? (a) naming a new leaf (b) 500 labelled leaf photos (c) labelled fruit photos (d) old emails marked spam
  37. 37.⏩ In machine learning, which comes first: what training builds or naming a new fruit photo? (a) what training builds (b) naming a new fruit photo
  38. 38.Which of these is a prediction? (a) old emails marked spam (b) 500 labelled leaf photos (c) flagging a new email (d) recorded spoken words
  39. 39.True or false: “the learned patterns” is training data. (a) True (b) False
  40. 40.True or false: “the trained program” is the model. (a) False (b) True

Answer key

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

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