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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 689 · 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.🧪 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
  2. 2.⏩ In machine learning, which comes first: what training builds or naming a new leaf? (a) what training builds (b) naming a new leaf
  3. 3.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  4. 4.Which of these is training data? (a) tomorrow’s rain guess (b) naming a new fruit photo (c) typing a new spoken word (d) recorded spoken words
  5. 5.🔁 What is the right order? (a) model, prediction, data (b) prediction, data, model (c) data, prediction, model (d) data, model, prediction
  6. 6.Which of these is a prediction? (a) 500 labelled leaf photos (b) typing a new spoken word (c) recorded spoken words (d) old emails marked spam
  7. 7.⏩ In machine learning, which comes first: recorded spoken words or what training builds? (a) recorded spoken words (b) what training builds
  8. 8.Which of these is the model? (a) old emails marked spam (b) 500 labelled leaf photos (c) the learned patterns (d) typing a new spoken word
  9. 9.True or false: “naming a new leaf” is a prediction. (a) True (b) False
  10. 10.True or false: “naming a new leaf” is the model. (a) False (b) True
  11. 11.Which of these is a prediction? (a) past weather records (b) the learned patterns (c) naming a new fruit photo (d) what training builds
  12. 12.⏩ In machine learning, which comes first: the learned patterns or 500 labelled leaf photos? (a) the learned patterns (b) 500 labelled leaf photos
  13. 13.Which of these is the model? (a) tomorrow’s rain guess (b) 500 labelled leaf photos (c) naming a new fruit photo (d) the trained program
  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.⏩ In machine learning, which comes first: what training builds or recorded spoken words? (a) what training builds (b) recorded spoken words
  16. 16.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) a prediction (c) training data
  17. 17.🔁 Training data, the model or a prediction: “the trained program”? (a) training data (b) a prediction (c) the model
  18. 18.⏩ 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
  19. 19.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) a prediction (b) training data (c) the model
  20. 20.True or false: “flagging a new email” is a prediction. (a) True (b) False
  21. 21.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  22. 22.🔮 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
  23. 23.🔁 Training data, the model or a prediction: “naming a new fruit photo”? (a) training data (b) the model (c) a prediction
  24. 24.True or false: “tomorrow’s rain guess” is the model. (a) True (b) False
  25. 25.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) the model (c) training data
  26. 26.Which of these is training data? (a) 500 labelled leaf photos (b) what training builds (c) flagging a new email (d) tomorrow’s rain guess
  27. 27.🔁 Training data, the model or a prediction: “the learned patterns”? (a) training data (b) the model (c) a prediction
  28. 28.📦 What happens during training? (a) it charges up (b) it takes photos (c) it prints labels (d) it learns patterns
  29. 29.True or false: “old emails marked spam” is a prediction. (a) True (b) False
  30. 30.True or false: “past weather records” is training data. (a) True (b) False
  31. 31.True or false: “recorded spoken words” is the model. (a) True (b) False
  32. 32.True or false: “500 labelled leaf photos” is the model. (a) True (b) False
  33. 33.Which of these is a prediction? (a) the learned patterns (b) flagging a new email (c) labelled fruit photos (d) old emails marked spam
  34. 34.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) a prediction (c) training data
  35. 35.True or false: “naming a new fruit photo” is a prediction. (a) True (b) False
  36. 36.⏩ In machine learning, which comes first: the learned patterns or flagging a new email? (a) the learned patterns (b) flagging a new email
  37. 37.⏩ In machine learning, which comes first: naming a new leaf or 500 labelled leaf photos? (a) naming a new leaf (b) 500 labelled leaf photos
  38. 38.True or false: “naming a new fruit photo” is training data. (a) True (b) False
  39. 39.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
  40. 40.True or false: “recorded spoken words” is a prediction. (a) True (b) False

Answer key

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

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