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Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

The DBSCAN algorithm in action

DBSCAN is one of the clustering algorithms that can deal with non-flat geometry and uneven cluster sizes. Let's see what it can do:

from sklearn.cluster import DBSCANestimators = [{'estimator': DBSCAN, 'args':(), 'kwargs':{'eps': 0.5}}]unsupervised_learner = Unsupervised_AutoML(estimators)predictions, performance_metrics = unsupervised_learner.fit_predict(X, y)

Metrics in the console are as follows:

################## DBSCAN metrics #####################  Silhouette Coefficient: 0.231  Estimated number of clusters: 12.000  Homogeneity: 0.794  Completeness: 0.800  V-measure: 0.797  Adjusted Rand Index: 0.737  Adjusted Mutual Information: 0.792

DBSCAN clusters are plotted as follows:

plot_kwargs = {'s': 12, 'linewidths': ...
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Publisher Resources

ISBN: 9781788629898Supplemental Content