Industry News Details

The supervised approach to machine learning Posted on : Mar 06 - 2021

Supervised learning is one of the most widely used machine learning approaches. It can be useful for predicting financial results, detecting fraud, recognizing objects in images and evaluating or assessing risk. The aim of supervised learning is to allow machine learning functions to work in such a way that enables the input data to be used to predict the output class for each new data instance for which the classification is not already known.

With supervised learning, the input data and output data (also called the class) are known in advance. This allows the model to be trained so that it produces the best predictions of classes for the training data by knowing when the model did and did not make a classification error (Figure 1). Subsequent to training the model with the labeled data set, the trained model can then be used to classify future input data with unknown classification.

As part of a supervised learning process, the machine learning system is required to identify circles in images from the data it receives. The result is a form of predictive modeling, whereby the machine recognizes the circles from the other shapes in images.

As the machine learning system continues to make decisions based on the data presented to it, the results of its decisions are reviewed (supervised) by the algorithm. When incorrect decisions are made during training with the labeled data, the algorithm has the opportunity to make adjustments as part of the training process.

Once the model is trained and deployed, the machine learning system can make decisions based on new data it processes View More