In supervised learning, what type of data is used for training?

In supervised learning, the training process relies on labeled data. Labeled data consists of input-output pairs where the input data is presented alongside the correct output label. This means that each example in the dataset has both features (attributes or characteristics) and a corresponding target value or label that the model is tasked with predicting.

Using labeled data allows the learning algorithm to find patterns and relationships between the input features and their associated labels. Through this training process, the algorithm can learn to make predictions on new, unseen data by applying the associations it established during training. This contrasts with other types of data; for instance, unlabeled data does not contain these output labels, making it unsuitable for supervised learning tasks where the goal is to predict specific outcomes.

The use of labeled data is crucial for developing accurate and reliable predictive models in supervised learning contexts. This methodology enables practitioners to build models that can generalize well to new data by ensuring that the model understands the mapping between inputs and their known outputs.