Regression is one of the methods used in supervised learning. These models predict a continuous-valued output based on an independent input variable. In this article, we will dive into the main regression algorithms and understand the mathematics behind them.
In the more recent years, Linedata implemented machine learning algorithms in front-office products as well as in back-office solutions. In this article, we will focus on one of the use cases of supervised regression learning, implemented here at Linedata.
In the previous article, we understood the functioning and the uses of smart contracts. As the use of these modern contracts is growing on a regular basis, we will now focus on comparing these contracts with the traditional contracts that we have known for centuries.
Supervised learning is a predictive modelling technique, used in machine learning. It is characterized by the training of a model on a labeled or “training” dataset, which includes a set of input variables and their corresponding output. This allows the model to establish rules between the input and output data, in order to map new unlabelled inputs to their unknown output.
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