Understanding random forests: from theory to practice
A comprehensive PhD dissertation providing an in-depth theoretical and practical analysis of random forests, from algorithmic foundations to interpretability.
What is Understanding random forests: from theory to practice?
Understanding Random Forests is a PhD dissertation that provides a comprehensive analysis of random forest algorithms, examining their theoretical foundations, practical implementations, and interpretability characteristics. The work systematically investigates each component of random forests to shed light on their learning capabilities and limitations, with particular focus on variable importance measures and scalability considerations for large datasets.
Machine learning researchers, data scientists, and practitioners who want to deepen their theoretical understanding of random forest algorithms beyond surface-level implementation, particularly those interested in algorithmic interpretability and performance optimization.
This dissertation offers unique theoretical characterizations of random forest properties, including original complexity analyses and proofs about variable importance measures, combined with practical implementation insights from the Scikit-Learn library, making it a bridge between theoretical machine learning and applied data science.
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Repository of my thesis "Understanding Random Forests"
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