Practice Supervised Classification (3.5.1) - Satellite Image Processing

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8 questions

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2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is supervised classification?
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Think about how a teacher helps students learn.
Question 2.
Name one algorithm used in supervised classification.
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Consider what you've learned about probabilities.
3Question 3.
What does supervised classification require?
- Unlabeled data
- User-defined training data
- Random samples
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Consider how you might train a model to recognize objects.
4Question 4.
True or False: Support Vector Machines use multiple decision trees to classify data.
- True
- False
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Think about how SVM relates to different classes.
Question 5.
Given an unknown dataset of satellite images, describe how you would go about applying supervised classification. What steps would you take?
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Consider starting with labeling your known data.
Question 6.
Evaluate the impact of quality training data on the success of supervised classification. What might happen if the training data is biased?
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Reflect on how student biases affect assessment results.

4 more questions in this section. Enroll free to practise them all.