REMOVE_OUTLIERS
Removes data points that classify as outliers, based on the settings in the parameters.
Parameters

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.


threshold
– Specifies the threshold value to use when detecting outliers. ThesourceColumn
value is identified as an outlier if the score that's calculated with theoutlierStrategy
exceeds this number. The default is 3. 
removeType
– Specifies the way to remove the data. Valid values includeDELETE_ROWS
andCLEAR
. 
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 inminValue
andmaxValue
.


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.