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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 869 · 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.⏩ In machine learning, which comes first: the trained program or typing a new spoken word? (a) the trained program (b) typing a new spoken word
  2. 2.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  3. 3.🧪 Why test a model on NEW examples? (a) to make it slower (b) to change its name (c) to see if it works (d) to delete its data
  4. 4.⏩ In machine learning, which comes first: what training builds or tomorrow’s rain guess? (a) what training builds (b) tomorrow’s rain guess
  5. 5.True or false: “the trained program” is a prediction. (a) True (b) False
  6. 6.⏩ In machine learning, which comes first: the trained program or flagging a new email? (a) the trained program (b) flagging a new email
  7. 7.Which of these is a prediction? (a) labelled fruit photos (b) past weather records (c) recorded spoken words (d) naming a new fruit photo
  8. 8.True or false: “recorded spoken words” is a prediction. (a) False (b) True
  9. 9.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  10. 10.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new leaf (c) the learned patterns (d) labelled fruit photos
  11. 11.Which of these is the model? (a) naming a new leaf (b) past weather records (c) what training builds (d) recorded spoken words
  12. 12.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  13. 13.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) a prediction (c) training data
  14. 14.True or false: “the learned patterns” is a prediction. (a) False (b) True
  15. 15.True or false: “past weather records” is the model. (a) True (b) False
  16. 16.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, model, prediction (d) data, prediction, model
  17. 17.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it charges up (d) it learns patterns
  18. 18.Which of these is a prediction? (a) the trained program (b) the learned patterns (c) naming a new leaf (d) past weather records
  19. 19.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) a label error (c) a prediction (d) training data
  20. 20.True or false: “naming a new leaf” is the model. (a) False (b) True
  21. 21.Which of these is training data? (a) naming a new fruit photo (b) typing a new spoken word (c) what training builds (d) old emails marked spam
  22. 22.⏩ In machine learning, which comes first: old emails marked spam or the learned patterns? (a) old emails marked spam (b) the learned patterns
  23. 23.True or false: “typing a new spoken word” is the model. (a) True (b) False
  24. 24.⏩ In machine learning, which comes first: tomorrow’s rain guess or recorded spoken words? (a) tomorrow’s rain guess (b) recorded spoken words
  25. 25.⏩ In machine learning, which comes first: recorded spoken words or naming a new fruit photo? (a) recorded spoken words (b) naming a new fruit photo
  26. 26.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  27. 27.⏩ In machine learning, which comes first: naming a new leaf or labelled fruit photos? (a) naming a new leaf (b) labelled fruit photos
  28. 28.Which of these is training data? (a) past weather records (b) what training builds (c) tomorrow’s rain guess (d) the learned patterns
  29. 29.Which of these is a prediction? (a) old emails marked spam (b) 500 labelled leaf photos (c) labelled fruit photos (d) flagging a new email
  30. 30.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) a prediction (b) the model (c) training data
  31. 31.Which of these is the model? (a) 500 labelled leaf photos (b) typing a new spoken word (c) the trained program (d) recorded spoken words
  32. 32.⏩ In machine learning, which comes first: naming a new leaf or the trained program? (a) naming a new leaf (b) the trained program
  33. 33.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  34. 34.⏩ 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
  35. 35.Which of these is a prediction? (a) typing a new spoken word (b) past weather records (c) labelled fruit photos (d) what training builds
  36. 36.Which of these is the model? (a) labelled fruit photos (b) naming a new fruit photo (c) the learned patterns (d) tomorrow’s rain guess
  37. 37.Which of these is training data? (a) typing a new spoken word (b) what training builds (c) the learned patterns (d) 500 labelled leaf photos
  38. 38.True or false: “flagging a new email” is the model. (a) False (b) True
  39. 39.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  40. 40.True or false: “old emails marked spam” is the model. (a) True (b) False

Answer key

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

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