REMOVE_OUTLIERS
Removes data points that classify as outliers, based on the settings in the parameters.
Parameters
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sourceColumn– Specifies the name of an existing numeric column that might contain outliers. -
outlierStrategy– Specifies the approach to use in detecting outliers. Valid values include the following:-
Z_SCORE– Identifies a value as an outlier when it deviates from the mean by more than the standard deviation threshold. -
MODIFIED_Z_SCORE– Identifies a value as an outlier when it deviates from the median by more than the median absolute deviation threshold. -
IQR– Identifies a values as an outlier when it falls beyond the first and last quartile of column data. The interquartile range (IQR) measures where the middle 50% of the data points are.
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threshold– Specifies the threshold value to use when detecting outliers. ThesourceColumnvalue is identified as an outlier if the score that's calculated with theoutlierStrategyexceeds this number. The default is 3. -
removeType– Specifies the way to remove the data. Valid values includeDELETE_ROWSandCLEAR. -
trimValue– Specifies whether to remove all or some of the outliers. This Boolean value defaults toFALSE.-
FALSE– Removes all outliers -
TRUE– Removes outliers that rank outside of the percentile threshold specified inminValueandmaxValue.
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minValue– Indicates the minimum percentile value for the outlier range. Valid range is 0–100. -
maxValue– Indicates the maximum percentile value for the outlier range. Valid range is 0–100.
The following examples display syntax for a single RecipeAction operation. A recipe contains at least one RecipeStep operation, and a recipe step contains at least one recipe action. A recipe action runs the data transform that you specify. A group of recipe actions run in sequential order to create the final dataset.
