Skewness: Difference between revisions
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(Created page with "The '''skewness''' of a dataset determines the direction of the outliers. = Impact = Many models assume the data to be normally distributed. Skewed data in those models will result in inaccurate predictions. = Detection = Data skewness is detected during Exploratory data analysis. The first method is visualization. Just look at a graph lol. Numerically, in a dataset, if the median < the mean, then it is skewed to the right. Vice versa. = Mitigate pr...") |
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Data skewness is detected during [[Exploratory data analysis]]. | Data skewness is detected during [[Exploratory data analysis]]. | ||
The first method is visualization. Just look at a graph lol. | The first method is visualization. Just look at a graph lol. Histograms and box plots will get the job done. | ||
Numerically, in a dataset, if the median < the mean, then it is skewed to the right. Vice versa. | Numerically, in a dataset, if the median < the mean, then it is skewed to the right. Vice versa. | ||
= Mitigate problems = | = Mitigate problems = | ||
[[File:Skewness mitigation.png|thumb|Figure 1. Effects of log, square root, and inverse transformations on skewed data]] | |||
Skewed data can be transformed to approximate a more symmetric distribution. Examples include logarithmic, square root, and inverse transformations. | Skewed data can be transformed to approximate a more symmetric distribution. Examples include logarithmic, square root, and inverse transformations. | ||
[[Category:Machine Learning]] | [[Category:Machine Learning]] | ||
Latest revision as of 06:47, 26 April 2024
The skewness of a dataset determines the direction of the outliers.
Impact
Many models assume the data to be normally distributed. Skewed data in those models will result in inaccurate predictions.
Detection
Data skewness is detected during Exploratory data analysis.
The first method is visualization. Just look at a graph lol. Histograms and box plots will get the job done.
Numerically, in a dataset, if the median < the mean, then it is skewed to the right. Vice versa.
Mitigate problems
Skewed data can be transformed to approximate a more symmetric distribution. Examples include logarithmic, square root, and inverse transformations.
