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

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Challenge: Using DBSCAN Clustering to Detect Outliers

Oppgave

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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.

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Challenge: Using DBSCAN Clustering to Detect Outliers

Oppgave

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.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

close

Awesome!

Completion rate improved to 6.67

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