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AI Worksheet: Training Data, Model, Prediction

AI for students lesson 3: Training data, model and prediction · Set 271 · 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.

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

Answer key

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

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