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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 844 · 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: “recorded spoken words”? (a) a prediction (b) training data (c) the model
  2. 2.Which of these is training data? (a) naming a new leaf (b) naming a new fruit photo (c) 500 labelled leaf photos (d) tomorrow’s rain guess
  3. 3.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  4. 4.True or false: “naming a new leaf” is the model. (a) True (b) False
  5. 5.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  6. 6.⏩ In machine learning, which comes first: recorded spoken words or the learned patterns? (a) recorded spoken words (b) the learned patterns
  7. 7.True or false: “recorded spoken words” is a prediction. (a) True (b) False
  8. 8.🔁 What is the right order? (a) model, prediction, data (b) data, prediction, model (c) data, model, prediction (d) prediction, data, model
  9. 9.⏩ In machine learning, which comes first: typing a new spoken word or the learned patterns? (a) typing a new spoken word (b) the learned patterns
  10. 10.True or false: “old emails marked spam” is a prediction. (a) False (b) True
  11. 11.Which of these is a prediction? (a) the learned patterns (b) what training builds (c) tomorrow’s rain guess (d) recorded spoken words
  12. 12.Which of these is the model? (a) past weather records (b) recorded spoken words (c) naming a new leaf (d) the trained program
  13. 13.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) the model (b) training data (c) a prediction
  14. 14.⏩ In machine learning, which comes first: recorded spoken words or naming a new leaf? (a) recorded spoken words (b) naming a new leaf
  15. 15.🔮 A trained model sees a new photo and says “mango”. This is… (a) a label error (b) training data (c) a prediction (d) a password
  16. 16.🧪 Why test a model on NEW examples? (a) to see if it works (b) to delete its data (c) to make it slower (d) to change its name
  17. 17.True or false: “recorded spoken words” is the model. (a) False (b) True
  18. 18.⏩ In machine learning, which comes first: recorded spoken words or the trained program? (a) recorded spoken words (b) the trained program
  19. 19.Which of these is training data? (a) the learned patterns (b) flagging a new email (c) recorded spoken words (d) naming a new fruit photo
  20. 20.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  21. 21.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  22. 22.Which of these is the model? (a) past weather records (b) 500 labelled leaf photos (c) the learned patterns (d) old emails marked spam
  23. 23.True or false: “past weather records” is training data. (a) False (b) True
  24. 24.True or false: “old emails marked spam” is the model. (a) False (b) True
  25. 25.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data
  26. 26.True or false: “typing a new spoken word” is training data. (a) False (b) True
  27. 27.⏩ In machine learning, which comes first: naming a new leaf or what training builds? (a) naming a new leaf (b) what training builds
  28. 28.⏩ In machine learning, which comes first: tomorrow’s rain guess or labelled fruit photos? (a) tomorrow’s rain guess (b) labelled fruit photos
  29. 29.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) the model (b) training data (c) a prediction
  30. 30.⏩ In machine learning, which comes first: tomorrow’s rain guess or the trained program? (a) tomorrow’s rain guess (b) the trained program
  31. 31.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  32. 32.Which of these is a prediction? (a) 500 labelled leaf photos (b) naming a new leaf (c) labelled fruit photos (d) old emails marked spam
  33. 33.Which of these is training data? (a) the trained program (b) typing a new spoken word (c) past weather records (d) naming a new fruit photo
  34. 34.⏩ 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
  35. 35.Which of these is training data? (a) labelled fruit photos (b) flagging a new email (c) what training builds (d) tomorrow’s rain guess
  36. 36.Which of these is a prediction? (a) flagging a new email (b) 500 labelled leaf photos (c) old emails marked spam (d) past weather records
  37. 37.Which of these is a prediction? (a) past weather records (b) the learned patterns (c) labelled fruit photos (d) typing a new spoken word
  38. 38.⏩ In machine learning, which comes first: the trained program or recorded spoken words? (a) the trained program (b) recorded spoken words
  39. 39.True or false: “the trained program” is the model. (a) False (b) True
  40. 40.⏩ In machine learning, which comes first: naming a new leaf or old emails marked spam? (a) naming a new leaf (b) old emails marked spam

Answer key

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

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