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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 809 · 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: “old emails marked spam”? (a) training data (b) the model (c) a prediction
  2. 2.⏩ In machine learning, which comes first: typing a new spoken word or old emails marked spam? (a) typing a new spoken word (b) old emails marked spam
  3. 3.Which of these is the model? (a) naming a new leaf (b) tomorrow’s rain guess (c) the trained program (d) typing a new spoken word
  4. 4.⏩ In machine learning, which comes first: past weather records or what training builds? (a) past weather records (b) what training builds
  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: tomorrow’s rain guess or labelled fruit photos? (a) tomorrow’s rain guess (b) labelled fruit photos
  7. 7.True or false: “what training builds” is the model. (a) True (b) False
  8. 8.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
  9. 9.⏩ In machine learning, which comes first: naming a new fruit photo or the learned patterns? (a) naming a new fruit photo (b) the learned patterns
  10. 10.Which of these is training data? (a) flagging a new email (b) the trained program (c) recorded spoken words (d) naming a new leaf
  11. 11.True or false: “tomorrow’s rain guess” is the model. (a) True (b) False
  12. 12.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) training data (b) the model (c) a prediction
  13. 13.🧪 Why test a model on NEW examples? (a) to delete its data (b) to see if it works (c) to change its name (d) to make it slower
  14. 14.⏩ In machine learning, which comes first: the learned patterns or naming a new leaf? (a) the learned patterns (b) naming a new leaf
  15. 15.⏩ In machine learning, which comes first: typing a new spoken word or 500 labelled leaf photos? (a) typing a new spoken word (b) 500 labelled leaf photos
  16. 16.True or false: “typing a new spoken word” is training data. (a) True (b) False
  17. 17.Which of these is a prediction? (a) naming a new leaf (b) old emails marked spam (c) the trained program (d) the learned patterns
  18. 18.Which of these is a prediction? (a) recorded spoken words (b) old emails marked spam (c) past weather records (d) typing a new spoken word
  19. 19.📦 What happens during training? (a) it charges up (b) it prints labels (c) it learns patterns (d) it takes photos
  20. 20.True or false: “tomorrow’s rain guess” is a prediction. (a) True (b) False
  21. 21.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) training data (b) the model (c) a prediction
  22. 22.⏩ In machine learning, which comes first: past weather records or naming a new leaf? (a) past weather records (b) naming a new leaf
  23. 23.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  24. 24.⏩ In machine learning, which comes first: typing a new spoken word or past weather records? (a) typing a new spoken word (b) past weather records
  25. 25.⏩ In machine learning, which comes first: the learned patterns or old emails marked spam? (a) the learned patterns (b) old emails marked spam
  26. 26.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  27. 27.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  28. 28.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) training data (c) the model
  29. 29.Which of these is a prediction? (a) old emails marked spam (b) tomorrow’s rain guess (c) the learned patterns (d) 500 labelled leaf photos
  30. 30.Which of these is the model? (a) recorded spoken words (b) naming a new leaf (c) flagging a new email (d) the learned patterns
  31. 31.⏩ In machine learning, which comes first: the learned patterns or 500 labelled leaf photos? (a) the learned patterns (b) 500 labelled leaf photos
  32. 32.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  33. 33.⏩ In machine learning, which comes first: what training builds or typing a new spoken word? (a) what training builds (b) typing a new spoken word
  34. 34.🔁 What is the right order? (a) prediction, data, model (b) data, prediction, model (c) model, prediction, data (d) data, model, prediction
  35. 35.True or false: “past weather records” is training data. (a) True (b) False
  36. 36.True or false: “the trained program” is a prediction. (a) False (b) True
  37. 37.True or false: “the learned patterns” is the model. (a) False (b) True
  38. 38.True or false: “past weather records” is the model. (a) True (b) False
  39. 39.True or false: “old emails marked spam” is training data. (a) True (b) False
  40. 40.Which of these is a prediction? (a) naming a new fruit photo (b) the trained program (c) what training builds (d) 500 labelled leaf photos

Answer key

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

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