Assume there are predictor attributes, x1, x2, .... xn, and also an objective attribute, y, for a given dataset. Then, the supervised learning is themachine learning task of finding the prediction function that takes as input both the predictor attributes and the objective attribute from this dataset, and is capable of mapping the predictive attributes to the objective attribute for even unseen data currently not in the training dataset with minimal error.
The data in the dataset used for arriving at the prediction function is called the training data and it consists of a set of training examples where each example consists of an input object, x (typically a vector), and a desired output value, Y. A supervised learning algorithm analyzes the training data and produces an inferred function that maps the input to output and could also be used for mapping new, unseen example data:
Y = f(X) + error
The whole category of algorithms is called supervised...
