Machine Learning for Sustainable Development by Kamal Kant Hiran Deepak Khazanchi Ajay Kumar Vyas Sanjeevikumar Padmanaban
Author:Kamal Kant Hiran, Deepak Khazanchi, Ajay Kumar Vyas, Sanjeevikumar Padmanaban
Language: eng
Format: epub
Publisher: De Gruyter
Published: 2021-06-07T02:14:45.382000+00:00
Figure 5.7: Ensemble classifier (combination of different classifiers).
5.3
Unsupervised learning
Unsupervised learning is the field of ML algorithms that are used to extract the implications as outputs from the datasets without having input labels. Unsupervised learning consists of clustering, dimensionality reduction, deep learning, recommender systems, etc. In this learning, data are available in different clusters and those are formed by dividing the data based on similar features called clustering. Unsupervised learning is more skewed than SL because this learning is not so easy as prediction is found in the supervised, though it acquires unlabeled data instead of labeled so that human interference is less. Furthermore, it works on large databases such as in the medical field, online browsing data and online shopping data.
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