# Numeric Outliers

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 (Q_{1}, Q_{3}) is computed. An observation is flagged an outlier if it lies outside the range R = [Q_{1} - k(IQR), Q_{3} + k(IQR)] with IQR = Q_{3} - Q_{1} 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.

### Input Ports

- Type: Data Numeric input data to evaluate + optional group information

### Output Ports

- Type: Data Data table where outliers were either replaced or rows containing outliers/non-outliers were removed
- Type: Data 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
- Type: Outlier Model holding the permitted interval bounds for each outlier group and the outlier treatment specifications

## Find here

Analytics > Statistics

Make sure to have this extension installed:

## KNIME Core

Update site for KNIME Analytics Platform 3.7:

KNIME Analytics Platform 3.7 Update Site