Supervised Learning

Supervised learning algorithms take a dataset and use its features to learn some relationship with a corresponding set of labels. This process is known as training and, once complete, we would hope that our algorithm would do a good job of predicting the labels of brand new data in which the algorithm has no explicit knowledge of the true label. For example, we might train a supervised algorithm using a set of images of common animals as well as their corresponding labels (e.g. dog, cat, chicken). The algorithm will exploit useful features from the images such as number of legs or colour to find useful patterns that link images with their correct labels. After successful training we can use the fully trained algorithm to attempt to predict the labels of a brand new set of unseen images. We generally judge the performance of the algorithm by its accuracy in prediction of these new unseen images. Supervised learning can be applied to a wide range of problems such as email spam detection or stock price prediction. The Decision Tree is an example of a supervised learning algorithm.

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