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What is the key difference between Supervised Learning and Unsupervised Learning.
Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset, where the desired output is already known. The algorithm uses this training data to learn the relationship between the input features and the output, and then applies this knowledge to make predictions on new, unseen data. Supervised learning is commonly used for tasks such as classification and regression.
In contrast, unsupervised learning is a type of machine learning where the algorithm is trained on an unlabeled dataset, where the desired output is not known. The algorithm tries to identify patterns or relationships in the data without any prior knowledge of what these patterns or relationships might be. Unsupervised learning is commonly used for tasks such as clustering and dimensionality reduction.
The main difference between supervised and unsupervised learning is the presence or absence of labeled data. In supervised learning, the algorithm has access to labeled data and uses this information to make predictions. In unsupervised learning, the algorithm does not have access to labeled data and must instead try to identify patterns or relationships in the data on its own.
The term "master data" refers to information that is shared across an organization. There are several kinds of master data. For example, a company uses enterprise master data all over the world, whereas markets rely on market master data all over the world. In addition to the aforementioned, reference data is a sort of master data. Master data includes customers, products, staff, materials, and suppliers.
Managing master data is the most important procedure for improving master data quality. Furthermore, the quality of master data has a significant impact on analytical results and reports. Typically, master data is stored in central repositories. This data is used by one or more other systems. Because this data is used by different parties, there may be duplicates. It may result in data inconsistencies.
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