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.