Author: Axel Preis
Supervised Learning – an AI Learns With the Support of a Human
»Supervised Learning« is the term used by experts to describe methods that learn using a labeled data set. A labeled data set consists of labels and features. Labels are the manifestations of a variable that is of primary interest and should be predicted, for example, fraud cases in a booking system. Features, in turn, are variables that are used to explain the variable of interest. Supervised Learning methods learn the relationship between features and labels from the data. Often, experts are used to create labels. So human intelligence comes into play first to make the learning possible at all. These people assign a label to each data point.
Providing the data is thus often very time-consuming – especially in times of Big Data, where data sets can consist of millions of data points. As we can see, supervised ML methods require labels during the learning process that are generated under human supervision, which is why we speak of Supervised Learning.
Application Example: Artificial Intelligence in Detecting Fraud in Booking Systems
An example of the use of Supervised Learning, which we in the EP-KI team often deal with, is the detection of fraud in a booking system. A corresponding AI system decides whether a booking is considered suspicious or not based on factors such as »amount of money« or »date of booking«. These factors form the features of a possible ML model. For such decisions, it needs booking data for learning that has already been labeled as »fraud« or »no fraud«. These markings represent the labels. An ML model thus learns whether new bookings are potential fraud based on the features and labels of the dataset.