This node detects and treats the outliers for each of the selected columns individually by means of interquartile range (IQR).
To detect the outliers for a given column, the first and third quartile (Q1, Q3)
is computed. An observation is flagged an outlier if it lies outside the range R = [Q1 -
k(IQR), Q3 + k(IQR)] with IQR = Q3 - Q1 and k >= 0. Setting k = 1.5
the smallest value in R corresponds, typically, to the lower end of a boxplot's whisker and largest
value to its upper end.
Providing grouping information allows to detect outliers only within
their respective groups.
If an observation is flagged an outlier, one can either replace it by some other value or remove/retain the corresponding row.
Missing values contained in the data will be ignored, i.e., they will neither be used for the outlier computation nor will they be flagged as an outlier.
Options
Output Ports
- Data table where outliers were either replaced or rows containing outliers/non-outliers were removed.
- Data table holding the number of members, i.e., non-missing values and outliers as well as the lower and upper bound for each outlier groups.
- Model holding the permitted interval bounds for each outlier group and the outlier treatment specifications.
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Workflows
!Modern UI_ABC-XYX-RFM AutoML SuperstoreKNIME Hub- 00. Install ExtensionsKNIME Hub
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Links
- Remove Outliersby mlauber71 on 2018-12-18forum.knime.com/p/51685
- Calculating averageby qqilihq on 2019-05-14forum.knime.com/p/60474
- Help with a Workflowby izaychik63 on 2018-12-18forum.knime.com/p/51709
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