Authors :
SMM Lakmali
Volume/Issue :
Volume 9 - 2024, Issue 5 - May
Google Scholar :
https://tinyurl.com/ytjk7vx5
Scribd :
https://tinyurl.com/56mjd24b
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAY1629
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Underscoring the interwoven methodologies
and shared objectives of Machine Learning (ML) and
Statistics, this paper aims to explore the synergy between
the two disciplines with the proliferation of large datasets
and advanced computational power. The ability of
observing accurate insights for complex datasets and
addressing real world applications with sophisticated,
hybrid approaches can enhance with the convergence of
the ML and Statistics. Although the concepts of both
disciplines started with distinct origins, two disciplines
increasingly intersect, fostering methodological cross
fertilization. To improve the generalization and
interpretability of ML concepts, statistical techniques
such as model selection and regularization can be used
while ensemble methods and neural networks exemplify
predictive modeling’s statistical applications. By
integrating ML to address the challenges in statistics such
as fairness, interpretability, robustness, and scalability,
statistician can enhance the key feature of statistics more
effectively. Overall, combined concepts of ML and
statistics not only address the diverse analytical task but
it pave the path for Artificial Intelligence and data science
by highlighting the main role of their synergy in modern
data exploration.
Keywords :
Machine Learning, Statistics, Algorithms, Decision Making.
References :
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Underscoring the interwoven methodologies
and shared objectives of Machine Learning (ML) and
Statistics, this paper aims to explore the synergy between
the two disciplines with the proliferation of large datasets
and advanced computational power. The ability of
observing accurate insights for complex datasets and
addressing real world applications with sophisticated,
hybrid approaches can enhance with the convergence of
the ML and Statistics. Although the concepts of both
disciplines started with distinct origins, two disciplines
increasingly intersect, fostering methodological cross
fertilization. To improve the generalization and
interpretability of ML concepts, statistical techniques
such as model selection and regularization can be used
while ensemble methods and neural networks exemplify
predictive modeling’s statistical applications. By
integrating ML to address the challenges in statistics such
as fairness, interpretability, robustness, and scalability,
statistician can enhance the key feature of statistics more
effectively. Overall, combined concepts of ML and
statistics not only address the diverse analytical task but
it pave the path for Artificial Intelligence and data science
by highlighting the main role of their synergy in modern
data exploration.
Keywords :
Machine Learning, Statistics, Algorithms, Decision Making.