
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Pearson’s Correlation in Predictive Analytics and Machine Learning: Applications & Limitations
Author(s) | Jarita Das |
---|---|
Country | India |
Abstract | Pearson’s correlation coefficient serves as a vital statistical measure in predictive analytics and machine learning by offering profound insights into linear relationships between variables. It is, thus, instrumental in understanding variable relationships, selecting features, detecting multicollinearity, and assessing the model performance. This paper explores the applications of Pearson’s correlation in selecting features, reducing dimensionality, and interpreting the selected model. The paper highlights importance of Pearson’s correlation in identifying suitable predictors and improving algorithmic performance in predictive analytics and machine learning. The paper also takes into account the limitations of Pearson’s correlation that includes its sensitivity to outliers and reliance on assumptions of linearity and normality at the exclusion of non-linear associations. Alternative correlation measures like Spearman’s rank and mutual information that address the shortcomings of Pearson’s correlation are also taken within the purview of discussion. Through an examination of both the strengths and weaknesses of Pearson’s correlation, the paper sheds light into use of Pearson’s correlation in predictive modeling while stressing the need for adhering to complementary techniques in advanced machine learning applications. |
Keywords | Pearson’s correlation, predictive analytics, machine learning, Spearman’s rank, Kendall’s Tau |
Field | Mathematics > Statistics |
Published In | Volume 5, Issue 3, May-June 2023 |
Published On | 2023-05-06 |
DOI | https://doi.org/10.36948/ijfmr.2023.v05i03.48969 |
Short DOI | https://doi.org/g9qxdj |
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E-ISSN 2582-2160

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