The training set is a subset of data that is specifically used to teach a machine-learning model how to make predictions or perform tasks. The training set consists of examples of input data, along with their corresponding known outputs or labels. The primary purpose of the training set is to enable the model to learn patterns, relationships, and representations in the data, so it can generalize its understanding to new, unseen examples.
A comprehensive and diverse training set helps the model capture a wide range of scenarios and variations within the data, making it more robust. During the training process, the model is exposed to these examples multiple times, adjusting its internal parameters to minimize the differences between its predictions and the actual labels. This optimization process allows the model to learn from its mistakes and improve its performance over time. The training set is an essential component of the machine learning workflow, forming the basis for model learning and adaptation. It’s carefully selected and curated to ensure that the model acquires the necessary knowledge to accurately predict outcomes or perform tasks
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