map.outliers: Creates mapping tables for detecting outlier values in...
In XanderHorn/lazy: Automated data pre-processing and machine learning

Description Usage Arguments Value Author(s) Examples

Identifies outliers by using either tukey or a percentile based approach. For each feature any value greater than or less than the outlier values are seen as outliers.

1 2	map.outliers(data, x, outlier.mode = "tukey", lower.percentile = 0.05, upper.percentile = 0.95, progress = TRUE)

`data`	[required \| data.frame] Dataset containing features
`x`	[required \| character] A vector of numeric feature names present in the dataset
`outlier.mode`	[optional \| character \| default="tukey"] Mode to identify outliers. Options are tukey or percentile to identify outliers.
`lower.percentile`	[optional \| numeric \| default=0.05] Lower percentile used to identify outliers if mode is set to percentile
`upper.percentile`	[optional \| numeric \| default=0.95] Upper percentile used to identify outliers if mode is set to percentile
`progress`	[optional \| logical \| default=TRUE] Display a progress bar