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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 63 · 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.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new fruit photo (c) labelled fruit photos (d) the trained program
  2. 2.Which of these is the model? (a) flagging a new email (b) 500 labelled leaf photos (c) old emails marked spam (d) the trained program
  3. 3.🧪 Why test a model on NEW examples? (a) to make it slower (b) to see if it works (c) to delete its data (d) to change its name
  4. 4.True or false: “what training builds” is the model. (a) False (b) True
  5. 5.Which of these is training data? (a) tomorrow’s rain guess (b) the learned patterns (c) the trained program (d) 500 labelled leaf photos
  6. 6.⏩ In machine learning, which comes first: the learned patterns or tomorrow’s rain guess? (a) the learned patterns (b) tomorrow’s rain guess
  7. 7.⏩ In machine learning, which comes first: naming a new leaf or recorded spoken words? (a) naming a new leaf (b) recorded spoken words
  8. 8.⏩ In machine learning, which comes first: old emails marked spam or naming a new leaf? (a) old emails marked spam (b) naming a new leaf
  9. 9.Which of these is training data? (a) past weather records (b) typing a new spoken word (c) naming a new leaf (d) the learned patterns
  10. 10.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  11. 11.True or false: “past weather records” is the model. (a) True (b) False
  12. 12.True or false: “what training builds” is training data. (a) True (b) False
  13. 13.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) the model (c) training data
  14. 14.⏩ In machine learning, which comes first: the trained program or past weather records? (a) the trained program (b) past weather records
  15. 15.Which of these is a prediction? (a) what training builds (b) tomorrow’s rain guess (c) 500 labelled leaf photos (d) old emails marked spam
  16. 16.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) model, prediction, data (d) prediction, data, model
  17. 17.📦 What happens during training? (a) it charges up (b) it takes photos (c) it prints labels (d) it learns patterns
  18. 18.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) the model (b) a prediction (c) training data
  19. 19.🔁 Training data, the model or a prediction: “flagging a new email”? (a) a prediction (b) training data (c) the model
  20. 20.Which of these is the model? (a) the learned patterns (b) old emails marked spam (c) 500 labelled leaf photos (d) flagging a new email
  21. 21.Which of these is a prediction? (a) recorded spoken words (b) what training builds (c) 500 labelled leaf photos (d) naming a new leaf
  22. 22.🔮 A trained model sees a new photo and says “mango”. This is… (a) a password (b) a prediction (c) a label error (d) training data
  23. 23.⏩ In machine learning, which comes first: the learned patterns or past weather records? (a) the learned patterns (b) past weather records
  24. 24.🔁 Training data, the model or a prediction: “what training builds”? (a) a prediction (b) the model (c) training data
  25. 25.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  26. 26.⏩ In machine learning, which comes first: past weather records or naming a new leaf? (a) past weather records (b) naming a new leaf
  27. 27.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  28. 28.Which of these is a prediction? (a) 500 labelled leaf photos (b) the trained program (c) past weather records (d) flagging a new email
  29. 29.True or false: “what training builds” is a prediction. (a) False (b) True
  30. 30.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) a prediction (c) the model
  31. 31.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
  32. 32.True or false: “flagging a new email” is the model. (a) False (b) True
  33. 33.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  34. 34.🔁 Training data, the model or a prediction: “past weather records”? (a) training data (b) a prediction (c) the model
  35. 35.True or false: “naming a new leaf” is training data. (a) True (b) False
  36. 36.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  37. 37.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
  38. 38.⏩ In machine learning, which comes first: labelled fruit photos or the learned patterns? (a) labelled fruit photos (b) the learned patterns
  39. 39.True or false: “past weather records” is training data. (a) True (b) False
  40. 40.🔁 What is the right order? (a) prediction, data, model (b) model, prediction, data (c) data, model, prediction (d) data, prediction, model

Answer key

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

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