Supervised learning

In supervised learning, the dataset consists of both the input data point and the expected output, commonly known as the label. The job of the algorithm is to learn a mapping function from inputs to expected outputs. The function could be a linear function such as y = mx + c or non-linear like y = ax3 + bx2 + cx + d, where y is the target output and x is the input. All the supervised learning tasks can be categorized into regression and classification.