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