the nuts and bolts of machine learning coursera week 3 quiz answers

Test your knowledge: Explore unsupervised learning and K-means

1. Fill in the blank: K-means is an unsupervised partitioning algorithm used to organize _____ data into clusters.

Answers

2. In k-means, what term describes the point at which each cluster is defined?

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3. A data professional is iterating on certain tasks that will enable them to create a k-means model. They continue doing this until the algorithm converges. Which step of the model-building process does this scenario represent?

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Test your knowledge: Evaluate a K-means model

4. In a k-means model, which evaluation metric represents the sum of the squared distances between each observation and its closest centroid?

Answers

Test your knowledge: Evaluate a K-means model

5. In a k-means model, which evaluation metric represents the sum of the squared distances between each observation and its closest centroid?

Answers

6. Fill in the blank: A data professional may use the _____ method to choose an optimal value for k. This is a tool for identifying the point at which the decrease in inertia starts to level off.

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7. A data professional is using Scikit-learn to create a k-means model. Which attribute will enable them to get the cluster assignments?

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Weekly challenge 3

8. Which of the following statements correctly describe key aspects of k-means? Select all that apply.

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Shuffle Q/A 1

9. A data professional is recalculating the centroid of each cluster. Which step of the model-creation process are they working in?

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10. Fill in the blank: In order to evaluate the intracluster space in a k-means model, a data professional uses the inertia metric. This is the _____ of the squared distances between each observation and its nearest centroid.

Answers

the nuts and bolts of machine learning coursera week 3 quiz answers

Test your knowledge: Explore unsupervised learning and K-means

1. Fill in the blank: K-means is an unsupervised partitioning algorithm used to organize _____ data into clusters.

2. In k-means, what term describes the point at which each cluster is defined?

3. A data professional is iterating on certain tasks that will enable them to create a k-means model. They continue doing this until the algorithm converges. Which step of the model-building process does this scenario represent?

Test your knowledge: Evaluate a K-means model

4. In a k-means model, which evaluation metric represents the sum of the squared distances between each observation and its closest centroid?

Test your knowledge: Evaluate a K-means model

5. In a k-means model, which evaluation metric represents the sum of the squared distances between each observation and its closest centroid?

6. Fill in the blank: A data professional may use the _____ method to choose an optimal value for k. This is a tool for identifying the point at which the decrease in inertia starts to level off.

7. A data professional is using Scikit-learn to create a k-means model. Which attribute will enable them to get the cluster assignments?

Weekly challenge 3

8. Which of the following statements correctly describe key aspects of k-means? Select all that apply.

Shuffle Q/A 1

9. A data professional is recalculating the centroid of each cluster. Which step of the model-creation process are they working in?

10. Fill in the blank: In order to evaluate the intracluster space in a k-means model, a data professional uses the inertia metric. This is the _____ of the squared distances between each observation and its nearest centroid.