Practice Common Classification Algorithms (2) - Classification Algorithms

Practice: Common Classification Algorithms

Lesson 2Classification Algorithms

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9 questions

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3 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
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What type of data is Logistic Regression suitable for?
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Think about scenarios with only two possible outcomes.
Question 2.
Name one advantage of Decision Trees.
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Consider how visual representations help understand results.
3Question 3.
What algorithm is primarily used for binary classification?
- Logistic Regression
- K-Means Clustering
- Linear Regression
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Remember the name suggests its purpose.
4Question 4.
True or False: Decision Trees can only handle linear data.
- True
- False
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Think about how trees split data.
Question 5.
Consider a dataset with features that do not follow a linear pattern. Explain which classification algorithm (Logistic Regression, Decision Trees, or KNN) would be most appropriate and why.
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Think about the characteristics each algorithm requires in the data.
Question 6.
Design a scenario where KNN might fail dramatically compared to Logistic Regression. Describe the scenario and why KNN would struggle.
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Evaluate the impact of 'curse of dimensionality'.

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