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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 361 · 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: “what training builds”? (a) training data (b) the model (c) a prediction
  2. 2.Which of these is the model? (a) old emails marked spam (b) the learned patterns (c) recorded spoken words (d) 500 labelled leaf photos
  3. 3.🔁 What is the right order? (a) data, model, prediction (b) data, prediction, model (c) model, prediction, data (d) prediction, data, model
  4. 4.🔁 Training data, the model or a prediction: “the learned patterns”? (a) the model (b) a prediction (c) training data
  5. 5.⏩ In machine learning, which comes first: old emails marked spam or the trained program? (a) old emails marked spam (b) the trained program
  6. 6.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a prediction (c) a label error (d) a password
  7. 7.⏩ In machine learning, which comes first: what training builds or past weather records? (a) what training builds (b) past weather records
  8. 8.🔁 Training data, the model or a prediction: “flagging a new email”? (a) the model (b) training data (c) a prediction
  9. 9.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) a prediction (b) training data (c) the model
  10. 10.⏩ In machine learning, which comes first: labelled fruit photos or tomorrow’s rain guess? (a) labelled fruit photos (b) tomorrow’s rain guess
  11. 11.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) a prediction (b) the model (c) training data
  12. 12.⏩ In machine learning, which comes first: typing a new spoken word or what training builds? (a) typing a new spoken word (b) what training builds
  13. 13.⏩ In machine learning, which comes first: flagging a new email or the trained program? (a) flagging a new email (b) the trained program
  14. 14.Which of these is a prediction? (a) past weather records (b) flagging a new email (c) 500 labelled leaf photos (d) what training builds
  15. 15.True or false: “labelled fruit photos” is the model. (a) True (b) False
  16. 16.Which of these is a prediction? (a) what training builds (b) recorded spoken words (c) tomorrow’s rain guess (d) 500 labelled leaf photos
  17. 17.🧪 Why test a model on NEW examples? (a) to make it slower (b) to delete its data (c) to change its name (d) to see if it works
  18. 18.True or false: “recorded spoken words” is a prediction. (a) False (b) True
  19. 19.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  20. 20.True or false: “what training builds” is the model. (a) True (b) False
  21. 21.Which of these is training data? (a) labelled fruit photos (b) naming a new fruit photo (c) the trained program (d) typing a new spoken word
  22. 22.Which of these is training data? (a) typing a new spoken word (b) the learned patterns (c) the trained program (d) recorded spoken words
  23. 23.Which of these is training data? (a) naming a new leaf (b) naming a new fruit photo (c) what training builds (d) old emails marked spam
  24. 24.True or false: “old emails marked spam” is the model. (a) True (b) False
  25. 25.⏩ In machine learning, which comes first: typing a new spoken word or recorded spoken words? (a) typing a new spoken word (b) recorded spoken words
  26. 26.Which of these is the model? (a) old emails marked spam (b) recorded spoken words (c) the trained program (d) flagging a new email
  27. 27.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) training data (c) a prediction
  28. 28.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) a prediction (b) training data (c) the model
  29. 29.Which of these is a prediction? (a) naming a new leaf (b) the trained program (c) 500 labelled leaf photos (d) past weather records
  30. 30.True or false: “flagging a new email” is training data. (a) False (b) True
  31. 31.Which of these is the model? (a) past weather records (b) tomorrow’s rain guess (c) naming a new fruit photo (d) what training builds
  32. 32.📦 What happens during training? (a) it prints labels (b) it takes photos (c) it learns patterns (d) it charges up
  33. 33.True or false: “naming a new fruit photo” is the model. (a) True (b) False
  34. 34.True or false: “flagging a new email” is a prediction. (a) False (b) True
  35. 35.Which of these is a prediction? (a) the learned patterns (b) labelled fruit photos (c) naming a new fruit photo (d) old emails marked spam
  36. 36.True or false: “typing a new spoken word” is the model. (a) False (b) True
  37. 37.🔁 Training data, the model or a prediction: “past weather records”? (a) the model (b) a prediction (c) training data
  38. 38.True or false: “typing a new spoken word” is training data. (a) True (b) False
  39. 39.True or false: “the learned patterns” is a prediction. (a) True (b) False
  40. 40.True or false: “what training builds” is a prediction. (a) False (b) True

Answer key

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

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