Random Forest: applicazione su dati assicurativi

La statistica, sospinta dalle nuove sfide della scienza e dell'industria, è un ambito in perenne sviluppo. L'aumento della massa di possibili informazioni da archiviare, da organizzare e analizzare ha dato vita al campo del "data mining". La possibilità di studiare e imparare dai dati ha portato ad una rivoluzione non solo nel campo statistico ma anche nel campo dell'informatica e dell'ingegneria. In questo contesto nasce il "machine learning", un campo di studi che esplora lo studio e la costruzione di algoritmi in base di comprendere e di fare previsioni. Lo scopo di quest tesi è quello di analizzare un algoritmo elaborato dal Professor Leo Breiman (2001), noto come Random Forest

The field of Statistics is constantly challenged by the problems that science and industry brings to its door. With the advent of computers and the information age, statistical problems have exploded both in size and complexity. Challenges in the areas of data storage, organization and searching have led to the field of "data mining". The challenges in learning from data have led to a revolution in the statistical sciences. Since computation plays such a key role, it is not surprising that much of this new development has been done by researchers in other fields such as computer science and engineering. In this contest, born sub-field of computer science, machine learning. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data, such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is closely related to computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. In this context of continuous growth of statistical learning, many algorithm are been developed to face the amount of data used in all the business context. The aim of this thesis is to analyze and show a powerful model, known as Random Forest, introduced by Leo Breiman in 2001.