What are Quantiles?
Quantiles divide the dataset into equal parts based on rank or percentile. They represent the values at certain points in a dataset sorted in increasing order. General quantiles include the median (50th percentile), quartiles (25th, 50th, and 75th percentiles), and percentiles (values ranging from 0 to 100).
In machine learning and data science, quantiles play an important role in understanding the data, detecting outliers and evaluating model performance.
Types of Quantiles
- Quartiles: Quartiles divide a dataset into four equal parts, representing the 25th, 50th (median), and 75th percentiles.
- Quintiles: Quintiles divide a dataset into five equal parts, each representing 20% of the data.
- Deciles: Deciles divide a dataset into ten equal parts, with each decile representing 10% of the data.
- Percentiles: Percentiles divide a dataset into 100 equal parts, with each percentile representing 1% of the data.
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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