Practice Principal Component Analysis (pca) (6.2.2) - Unsupervised Learning – Clustering & Dimensionality Reduction

Practice: Principal Component Analysis (PCA)

Lesson 6.2.2Unsupervised Learning – Clustering & Dimensionality Reduction

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10 questions

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4 cards from this section. Quick, good the night before a test.
Exercise
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does PCA stand for?
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Think about what 'P' stands for.
Question 2.
What is the first step in PCA?
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Consider what we often do to data before analyzing.
3Question 3.
What is the purpose of PCA?
- To increase dimensions
- To reduce dimensions while preserving variance
- To eliminate all data
- To classify data into categories
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Remember, PCA simplifies datasets.
4Question 4.
True or False: PCA requires the data to be normalized before applying the technique.
- True
- False
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Think about the first step in PCA.
Question 5.
You have a high-dimensional dataset containing features from several products. Describe how you would apply PCA to simplify your analysis and what insights you hope to gain.
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Think about the goals when simplifying a dataset.
Question 6.
Critique the use and limitation of PCA in machine learning, especially regarding its effectiveness with non-linear datasets.
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Consider how PCA aligns with the dataset’s inherent relationships.

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