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Apache Spark for Data Science Cookbook by Padma Priya Chitturi

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Supervised learning

Supervised learning deals with training algorithms with labeled data, inputs for which the outcome or target variables are known. It then predicts the outcome/target with the trained model for unseen future data. For example, historical e-mail data will have individual e-mails marked as ham or spam; this data is then used for training a model that can predict future e-mails as ham or spam. Supervised learning problems can be broadly divided into two major areas; classification and regression. Classification deals with predicting categorical variables or classes; for example, whether an e-mail is ham or spam or whether a customer is going to renew a subscription or not in a post-paid telecom subscription. This target variable ...

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