What is the definition of supervised machine learning?

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Supervised machine learning is characterized by its use of labeled datasets, where the model is trained on input-output pairs. The correct answer highlights that this process involves grouping data based on known outputs, which helps the model learn to make predictions or classifications.

In supervised learning, each data point in the training set has a corresponding label, or output, that the model aims to predict for unseen data. This approach enables the development of algorithms capable of identifying patterns or relationships in data based on the examples provided during training.

Predicting future data and classification tasks are components of supervised learning; however, emphasizing grouping data based on outputs captures the essence of the training process. It is also important to note that supervised machine learning is indeed related to data analysis, as it involves deriving insights and predictions from structured datasets. Therefore, the definition accurately reflects the fundamental principles of how supervised machine learning operates in practice.

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