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