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.
Oplossing
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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.
Oplossing
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande optiesBedankt voor je feedback!
single
Awesome!
Completion rate improved to 7.14Perform DBSCAN Clustering
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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.
Oplossing
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande optiesBedankt voor je feedback!
