Twenty Years of Random Forest: preliminary results of a systematic literature review

The Random Forest (RF) model consists of an ensemble classifier that produces many decision trees through the use of a randomly selected subset of samples and training variables. The RF model has assumed importance within the scientific community thanks to its performance. The accuracy of its classifications and prediction has allowed the use of RF in several research domains, which have benefited from it. The present study aims to provide a preliminary review of the whole sci- entific production characterized by all the publications citing the article ”Random Forest” by Breiman, 2001, in the last 20 years (2001-2021)

Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests

The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not allow users to understand how certain decisions are made. This drawback is identified with the term "Black-Box", as well as models that do not allow to interpret the internal work of certain ML techniques, thus discouraging their use. In a highly regulated and risk-averse context such as healthcare, although "trust" is not synonymous with decision and adoption, trusting an ML model is essential for its adoption. Many clinicians and health researchers feel uncomfortable with black box ML models, even if they achieve high degrees of diagnostic or prognostic accuracy. Therefore more and more research is being conducted on the functioning of these models. Our study focuses on the Random Forest (RF) model. It is one of the most performing and used methodologies in the context of ML approaches, in all fields of research from hard sciences to humanities. In the health context and in the evaluation of health policies, their use is limited by the impossibility of obtaining an interpretation of the causal links between predictors and response. This explains why we need to develop new techniques, tools, and approaches for reconstructing the causal relationships and interactions between predictors and response used in a RF model. Our research aims to perform a machine learning experiment on several medical datasets through a comparison between two methodologies, which are inTrees and NodeHarvest. They are the main approaches in the rules extraction framework. The contribution of our study is to identify, among the approaches to rule extraction, the best proposal for suggesting the appropriate choice to decision-makers in the health domain

A comparison among interpretative proposals for Random Forests

The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields. Despite its growing success, there are some limitations and disadvantages: the most significant is the lack of interpretability that does not allow users to understand how particular decisions are made. Our study focus on one of the best performing and most used models in the Machine Learning framework, the Random Forest model. It is known as an efficient model of ensemble learning, as it ensures high predictive precision, flexibility, and immediacy; it is recognized as an intuitive and understandable approach to the construction process, but it is also considered a Black Box model due to the large number of deep decision trees produced within it. The aim of this research is twofold. We present a survey about interpretative proposal for Random Forest and then we perform a machine learning experiment providing a comparison between two methodologies, inTrees, and NodeHarvest, that represent the main approaches in the rule extraction framework. The proposed experiment compares methods performance on six real datasets covering different data characteristics: n. of observations, balanced/unbalanced response, the presence of categorical and numerical predictors. This study contributes to picture a review of the methods and tools proposed for ensemble tree interpretation, and identify, in the class of rule extraction approaches, the best proposal