Outlier Detection & Treatment for Machine Learning Models
Author(s): Vaibhav Tummalapalli
Publication #: 2507003
Date of Publication: 22.06.2025
Country: USA
Pages: 1-8
Published In: Volume 11 Issue 3 June-2025
DOI: https://doi.org/10.5281/zenodo.16500050
Abstract
Outliers can significantly impact the performance of machine learning models by skewing data distributions and introducing noise. This paper explores various techniques for outlier detection and treatment, with a focus on Gaussian and non-Gaussian distributions, domain-specific limits, and visualizations. Practical implementation tips and Python code examples are provided to facilitate adoption in real-world scenarios
Keywords: Outlier Detection, Machine Learning, Python, IQR, Standard Deviation, KS Test, Median Absolute Deviation, Propensity Models.
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