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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

Agglomerative clustering algorithm in action

Our last try will be with an agglomerative clustering algorithm:

from sklearn.cluster import AgglomerativeClusteringestimators = [{'estimator': AgglomerativeClustering, 'args':(), 'kwargs':{'n_clusters': 4, 'linkage': 'ward'}}]unsupervised_learner = Unsupervised_AutoML(estimators)predictions, performance_metrics = unsupervised_learner.fit_predict(X, y)

Metrics in the console are as follows:

################## AgglomerativeClustering metrics #####################  Silhouette Coefficient: 0.546  Estimated number of clusters: 4.000  Homogeneity: 0.751  Completeness: 0.905  V-measure: 0.820  Adjusted Rand Index: 0.719  Adjusted Mutual Information: 0.750

AgglomerativeClustering clusters are plotted as follows: ...

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Publisher Resources

ISBN: 9781788629898Supplemental Content