Statistics versus Machine Learning [PDF]
Nature Methods, 2018 Two major goals in the study of biological systems are inference andprediction. Inference creates a mathematical model of the datageneration process to formalize our understanding or test ahypothesis about how the system behaves. Prediction aims atforecasting unobserved outcomes or future behavior, such as whethera mouse with a given phenotype will ...
Danilo Bzdok +2 more
semanticscholar +7 more sources
Representativeness in Statistics, Politics, and Machine Learning [PDF]
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021 Representativeness is a foundational yet slippery concept. Though familiar at first blush, it lacks a single precise meaning. Instead, meanings range from typical or characteristic, to a proportionate match between sample and population, to a more general sense of accuracy, generalizability, coverage, or inclusiveness.
Kyla Chasalow, Karen Levy
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Fighting Money Laundering With Statistics and Machine Learning [PDF]
IEEE Access, 2023 Accepted for publication in IEEE Access, vol. 11, pp.
Rasmus Ingemann Tuffveson Jensen +1 more
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Statistics for Machine Learning with Mathematica Applications
, 2023 In recent years, the field of statistics has experienced a surge in interest and application, largely due to significant advances in computer technology. This progress has led to remarkable developments in statistics methods and algorithms, enabling their widespread adoption across various disciplines.
M. M. Hammad
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Evaluation metrics and statistical tests for machine learning
Scientific ReportsAbstractResearch on different machine learning (ML) has become incredibly popular during the past few decades. However, for some researchers not familiar with statistics, it might be difficult to understand how to evaluate the performance of ML models and compare them with each other.
Oona Rainio, Jarmo Teuho, Riku Klén
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Balancing ethics and statistics: machine learning facilitates highly accurate classification of mice according to their trait anxiety with reduced sample sizes. [PDF]
Transl PsychiatryMiedema J +4 more
europepmc +3 more sources
Invasive and non-invasive variables prediction models for cardiovascular disease-specific mortality between machine learning vs. traditional statistics. [PDF]
Sci RepChoi S, Oh M, Lee DH, Jee SH, Jeon JY.
europepmc +3 more sources
Technology- enabled advance in the worlds of statistics, machine learning and data mining
New Zealand Science Review, 2023 Advances in digital computing continue to have large effects on all aspects of life and society, including science. These advances are possible because we have computer languages that translate directly into computational steps that can be implemented ...
J. Maindonald
semanticscholar +1 more source
Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics
Mathematics, 2023 Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary ...
Ke-Lin Du +3 more
semanticscholar +1 more source
Machine learning and statistical physics: preface
Journal of Physics A: Mathematical and Theoretical, 2020 The aim of this special issue is to provide a picture of the state-of-the-art and open challenges in machine learning from a statistical physics perspective, mainly that of disordered systems.
Adriano Barra +4 more
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