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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 994 · 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.🔮 A trained model sees a new photo and says “mango”. This is… (a) training data (b) a label error (c) a prediction (d) a password
  2. 2.Which of these is training data? (a) naming a new fruit photo (b) naming a new leaf (c) tomorrow’s rain guess (d) old emails marked spam
  3. 3.True or false: “what training builds” is a prediction. (a) True (b) False
  4. 4.📦 What happens during training? (a) it learns patterns (b) it takes photos (c) it charges up (d) it prints labels
  5. 5.🔁 Training data, the model or a prediction: “typing a new spoken word”? (a) the model (b) a prediction (c) training data
  6. 6.True or false: “500 labelled leaf photos” is a prediction. (a) False (b) True
  7. 7.⏩ In machine learning, which comes first: tomorrow’s rain guess or the trained program? (a) tomorrow’s rain guess (b) the trained program
  8. 8.🔁 Training data, the model or a prediction: “500 labelled leaf photos”? (a) the model (b) training data (c) a prediction
  9. 9.True or false: “labelled fruit photos” is training data. (a) False (b) True
  10. 10.Which of these is a prediction? (a) past weather records (b) flagging a new email (c) 500 labelled leaf photos (d) the trained program
  11. 11.⏩ In machine learning, which comes first: naming a new leaf or the learned patterns? (a) naming a new leaf (b) the learned patterns
  12. 12.🔁 Training data, the model or a prediction: “old emails marked spam”? (a) a prediction (b) training data (c) the model
  13. 13.True or false: “the trained program” is a prediction. (a) False (b) True
  14. 14.⏩ In machine learning, which comes first: the learned patterns or 500 labelled leaf photos? (a) the learned patterns (b) 500 labelled leaf photos
  15. 15.True or false: “the trained program” is the model. (a) False (b) True
  16. 16.Which of these is training data? (a) the trained program (b) 500 labelled leaf photos (c) flagging a new email (d) naming a new fruit photo
  17. 17.🔁 Training data, the model or a prediction: “labelled fruit photos”? (a) training data (b) a prediction (c) the model
  18. 18.🔁 Training data, the model or a prediction: “flagging a new email”? (a) training data (b) a prediction (c) the model
  19. 19.🔁 Training data, the model or a prediction: “recorded spoken words”? (a) the model (b) a prediction (c) training data
  20. 20.🔁 Training data, the model or a prediction: “what training builds”? (a) training data (b) a prediction (c) the model
  21. 21.True or false: “naming a new fruit photo” is a prediction. (a) False (b) True
  22. 22.True or false: “flagging a new email” is a prediction. (a) False (b) True
  23. 23.True or false: “labelled fruit photos” is the model. (a) False (b) True
  24. 24.🔁 Training data, the model or a prediction: “naming a new leaf”? (a) the model (b) training data (c) a prediction
  25. 25.⏩ In machine learning, which comes first: what training builds or old emails marked spam? (a) what training builds (b) old emails marked spam
  26. 26.⏩ In machine learning, which comes first: the learned patterns or naming a new fruit photo? (a) the learned patterns (b) naming a new fruit photo
  27. 27.⏩ In machine learning, which comes first: recorded spoken words or naming a new fruit photo? (a) recorded spoken words (b) naming a new fruit photo
  28. 28.🔁 Training data, the model or a prediction: “tomorrow’s rain guess”? (a) training data (b) the model (c) a prediction
  29. 29.Which of these is a prediction? (a) the trained program (b) what training builds (c) recorded spoken words (d) tomorrow’s rain guess
  30. 30.Which of these is training data? (a) naming a new leaf (b) recorded spoken words (c) the trained program (d) typing a new spoken word
  31. 31.🔁 What is the right order? (a) data, prediction, model (b) data, model, prediction (c) prediction, data, model (d) model, prediction, data
  32. 32.True or false: “tomorrow’s rain guess” is the model. (a) False (b) True
  33. 33.True or false: “typing a new spoken word” is a prediction. (a) True (b) False
  34. 34.True or false: “old emails marked spam” is a prediction. (a) True (b) False
  35. 35.Which of these is training data? (a) the learned patterns (b) the trained program (c) tomorrow’s rain guess (d) labelled fruit photos
  36. 36.🧪 Why test a model on NEW examples? (a) to delete its data (b) to change its name (c) to make it slower (d) to see if it works
  37. 37.⏩ In machine learning, which comes first: the trained program or recorded spoken words? (a) the trained program (b) recorded spoken words
  38. 38.True or false: “flagging a new email” is training data. (a) False (b) True
  39. 39.True or false: “tomorrow’s rain guess” is a prediction. (a) False (b) True
  40. 40.⏩ 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

Answer key

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

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