Training data is a fundamental concept in artificial intelligence (AI) that refers to the labeled dataset used to teach machine learning models to recognize patterns, make predictions, and perform tasks. The training data consists of input examples paired with their corresponding desired outputs or labels. This data serves as the foundation for the learning process, where the model adjusts its parameters based on the provided examples to learn how to generalize and make accurate predictions on new, unseen data.
The quality and representativeness of the training data have a profound impact on the performance and generalization capabilities of the trained model. A diverse and comprehensive training dataset helps the model learn a wide range of patterns and variations present in the data. The process of training involves iteratively feeding the training data to the model, calculating the prediction error, and updating the model’s parameters to minimize this error. Over time, the model learns to identify features, relationships, and patterns within the data that enable it to make accurate predictions.
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