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Learn Challenge: Using DBSCAN Clustering to Detect Outliers | Machine Learning Techniques
Data Anomaly Detection

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Challenge: Using DBSCAN Clustering to Detect Outliers

Task

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Now, you will apply the DBSCAN clustering algorithm to detect outliers on a simple Iris dataset.
You have to:

  1. Specify the parameters of the DBScan algorithm: set eps equal to 0.35 and min_samples equal to 6.
  2. Fit the algorithm and provide clustering.
  3. Get outlier indexes and indexes of normal data. Pay attention that outliers detected by the algorithm have a -1 label.

Solution

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book
Challenge: Using DBSCAN Clustering to Detect Outliers

Task

Swipe to start coding

Now, you will apply the DBSCAN clustering algorithm to detect outliers on a simple Iris dataset.
You have to:

  1. Specify the parameters of the DBScan algorithm: set eps equal to 0.35 and min_samples equal to 6.
  2. Fit the algorithm and provide clustering.
  3. Get outlier indexes and indexes of normal data. Pay attention that outliers detected by the algorithm have a -1 label.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

close

Awesome!

Completion rate improved to 6.67

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