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Clustering Demystified

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Now that we have trained our clustering algorithm we have to evaluate its performances to assess the results.

Methods description

  • .labels_: This attribute of the KMeans object contains the cluster labels assigned to each data point after fitting the KMeans algorithm to the data;
  • y == labels: This is a comparison operation that checks element-wise equality between two arrays y and labels, resulting in a boolean array;
  • correct_labels: This variable stores the count of data points that were correctly labeled, i.e., assigned to the correct cluster.
Task

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  1. Compare the correct_labels with the predicted ones.
  2. Print the results.

Solution

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SectionΒ 1. ChapterΒ 9

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bookCheck the Quality

Now that we have trained our clustering algorithm we have to evaluate its performances to assess the results.

Methods description

  • .labels_: This attribute of the KMeans object contains the cluster labels assigned to each data point after fitting the KMeans algorithm to the data;
  • y == labels: This is a comparison operation that checks element-wise equality between two arrays y and labels, resulting in a boolean array;
  • correct_labels: This variable stores the count of data points that were correctly labeled, i.e., assigned to the correct cluster.
Task

Swipe to start coding

  1. Compare the correct_labels with the predicted ones.
  2. Print the results.

Solution

Mark tasks as Completed
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Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 9
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