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

Kursusindhold

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
2. Statistical Methods in Anomaly Detection
3. Machine Learning Techniques

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Challenge: Using DBSCAN Clustering to Detect Outliers

Opgave

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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.

Løsning

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Var alt klart?

Hvordan kan vi forbedre det?

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Sektion 3. Kapitel 2
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book
Challenge: Using DBSCAN Clustering to Detect Outliers

Opgave

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 desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 2
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Vi beklager, at noget gik galt. Hvad skete der?
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