Steps to Calculate Quantiles
The steps for calculating quantiles involve:
- Sorting the Data: Arrange the dataset in increasing order.
- Determine the Position: Calculate the position of the desired quantile based on the given formula: “Position=(quantile×(n+1))/100”, where n is the total number of observations.
- Interpolation (if needed): Interpolate between two adjacent values to find the quantile if the position is not an integer.
Example with Mathematical Imputation:
Let’s consider a dataset: [5, 10, 15, 20, 25, 30, 35, 40, 45, 50].
- Median (Q2): There are 10 observations, so the median position is (2×(10+1))/2=5.5. Since, 5.5 is not an integer, we interpolate between the 5th and 6th observations: Median=(25+30)/2=27.5.
- First Quartile (Q1): (25×(10+1))/4=13.75. Interpolating between the 13th and 14th observations: Q1=(15+20)/2=17.5.
- Third Quartile (Q3):(75×(10+1))/4=41.25. Interpolating between the 41st and 42nd observations: Q3=(40+45)/2=42.5.
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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