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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 486 · 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: “past weather records”? (a) training data (b) a prediction (c) the model
  2. 2.⏩ In machine learning, which comes first: what training builds or 500 labelled leaf photos? (a) what training builds (b) 500 labelled leaf photos
  3. 3.Which of these is a prediction? (a) the learned patterns (b) flagging a new email (c) past weather records (d) labelled fruit photos
  4. 4.Which of these is a prediction? (a) 500 labelled leaf photos (b) labelled fruit photos (c) naming a new fruit photo (d) what training builds
  5. 5.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  6. 6.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  7. 7.⏩ In machine learning, which comes first: naming a new fruit photo or the trained program? (a) naming a new fruit photo (b) the trained program
  8. 8.📦 What happens during training? (a) it takes photos (b) it charges up (c) it learns patterns (d) it prints labels
  9. 9.True or false: “recorded spoken words” is the model. (a) False (b) True
  10. 10.Which of these is the model? (a) tomorrow’s rain guess (b) typing a new spoken word (c) what training builds (d) old emails marked spam
  11. 11.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) a prediction (c) the model
  12. 12.🧪 Why test a model on NEW examples? (a) to change its name (b) to delete its data (c) to see if it works (d) to make it slower
  13. 13.🔁 Training data, the model or a prediction: “the trained program”? (a) a prediction (b) training data (c) the model
  14. 14.True or false: “recorded spoken words” is training data. (a) True (b) False
  15. 15.Which of these is training data? (a) the learned patterns (b) labelled fruit photos (c) naming a new leaf (d) typing a new spoken word
  16. 16.True or false: “the trained program” is training data. (a) False (b) True
  17. 17.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) training data (b) the model (c) a prediction
  18. 18.🔮 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
  19. 19.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  20. 20.⏩ In machine learning, which comes first: labelled fruit photos or naming a new leaf? (a) labelled fruit photos (b) naming a new leaf
  21. 21.Which of these is the model? (a) past weather records (b) tomorrow’s rain guess (c) the trained program (d) naming a new leaf
  22. 22.⏩ In machine learning, which comes first: labelled fruit photos or typing a new spoken word? (a) labelled fruit photos (b) typing a new spoken word
  23. 23.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  24. 24.⏩ In machine learning, which comes first: old emails marked spam or tomorrow’s rain guess? (a) old emails marked spam (b) tomorrow’s rain guess
  25. 25.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  26. 26.🔁 What is the right order? (a) model, prediction, data (b) data, model, prediction (c) data, prediction, model (d) prediction, data, model
  27. 27.True or false: “past weather records” is the model. (a) True (b) False
  28. 28.Which of these is the model? (a) tomorrow’s rain guess (b) the learned patterns (c) old emails marked spam (d) flagging a new email
  29. 29.⏩ In machine learning, which comes first: 500 labelled leaf photos or the learned patterns? (a) 500 labelled leaf photos (b) the learned patterns
  30. 30.⏩ In machine learning, which comes first: flagging a new email or recorded spoken words? (a) flagging a new email (b) recorded spoken words
  31. 31.True or false: “what training builds” is the model. (a) False (b) True
  32. 32.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new leaf (c) the trained program (d) past weather records
  33. 33.Which of these is training data? (a) the trained program (b) recorded spoken words (c) the learned patterns (d) flagging a new email
  34. 34.True or false: “the learned patterns” is the model. (a) False (b) True
  35. 35.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  36. 36.True or false: “what training builds” is training data. (a) True (b) False
  37. 37.Which of these is a prediction? (a) recorded spoken words (b) tomorrow’s rain guess (c) old emails marked spam (d) labelled fruit photos
  38. 38.⏩ In machine learning, which comes first: tomorrow’s rain guess or what training builds? (a) tomorrow’s rain guess (b) what training builds
  39. 39.⏩ 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
  40. 40.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) training data (c) the model

Answer key

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

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