What is supervised learning, and how is it implemented in PyBrain?
Supervised learning is a type of machine learning where a model learns to map input data to corresponding output labels based on a training dataset consisting of input-output pairs. The goal of supervised learning is to learn a mapping function from input to output that generalizes well to unseen data.
In PyBrain, supervised learning is implemented using the SupervisedDataSet class to represent the training dataset and various supervised learning algorithms such as backpropagation for training neural networks.
