In supervised learning techniques, what do the training data consist of?

In supervised learning techniques, the training data consists of vector pairs that include an input vector and a target vector. This structure is essential because it allows the learning algorithm to understand the relationship between the inputs and the desired outputs. The input vector contains the features or attributes used to make predictions, while the target vector contains the corresponding labels or outcomes that the model is trained to predict.

When the algorithm is provided with these pairs during the training phase, it learns to map the input data to the associated target. This process is crucial for building a model that can generalize well to new, unseen data, as it has a clear understanding of what the expected result should be for each input.

The other options do not accurately describe the composition of training data in supervised learning. Single input vectors lack the necessary context of corresponding outputs, making it impossible for the model to learn how to associate inputs with outputs. Similarly, groups of target vectors would not provide any input data for the algorithm, rendering the training ineffective. A pair of input and feedback vectors may imply an aspect of feedback learning but does not capture the critical distinction of input-output relationships inherent in supervised learning.