Perform DBSCAN Clustering
Swipe to start coding
As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.
Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:
- Import the
DBSCANclass fromsklearn.clustermodule. - Use DBSCAN class and
.fit()method of this class. - Use
.labels_attribute of DBSCAN class. - Specify
clustering.labels_==-1to detect noise.
Ratkaisu
Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoistaKiitos palautteestasi!
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Completion rate improved to 7.14Awesome!
Completion rate improved to 7.14Perform DBSCAN Clustering
Swipe to start coding
As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.
Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:
- Import the
DBSCANclass fromsklearn.clustermodule. - Use DBSCAN class and
.fit()method of this class. - Use
.labels_attribute of DBSCAN class. - Specify
clustering.labels_==-1to detect noise.
Ratkaisu
Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoistaKiitos palautteestasi!
single
Awesome!
Completion rate improved to 7.14Perform DBSCAN Clustering
Pyyhkäise näyttääksesi valikon
Swipe to start coding
As we mentioned in the previous chapter, DBSCAN algorithm classifies points as core, border, and noise. As a result, we can use this algorithm to clean our data from outliers. Let's create DBSCAN model, clean data, and look at the results.
Your task is to train DBSCAN model on the circles dataset, detect noise points, and remove them. Look at the visualization and compare data before and after cleaning. You have to:
- Import the
DBSCANclass fromsklearn.clustermodule. - Use DBSCAN class and
.fit()method of this class. - Use
.labels_attribute of DBSCAN class. - Specify
clustering.labels_==-1to detect noise.
Ratkaisu
Vaihda työpöytään todellista harjoitusta vartenJatka siitä, missä olet käyttämällä jotakin alla olevista vaihtoehdoistaKiitos palautteestasi!
