Challenges and Limitations of Quantiles
- Influence of Outliers: Quantiles can be sensitive to outliers, especially when calculating quartiles. Outliers can significantly affect the position of quantiles, potentially leading to a misrepresentation of the data’s central tendency and spread.
- Skewed Distributions: Quantiles may not fully capture the characteristics of skewed distributions. For highly skewed datasets, the quantiles may not provide a complete picture of the data distribution, especially in the tails.
- Variability in Calculations: Different methods and software packages may use different algorithms for calculating quantiles, leading to variability in results. This can be a challenge when comparing quantiles across different datasets or when using quantiles for decision-making.
Quantiles in Machine Learning
Quantiles offers valuable insights into data distribution and helping in various aspects of analysis. This article describes quantiles, looks at how to calculate them, and talks about how important they are for machine learning applications. We also discuss the problems with quantiles and how box plots may be used to represent them. For anybody dealing with data in the field of machine learning, having a firm understanding of quantiles is crucial.
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