> For the complete documentation index, see [llms.txt](https://docs.phoenixlidar.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.phoenixlidar.com/spatialexplorer-8-and-9/user-interface/toolbars/analytics/classify/classify-statistical-outliers.md).

# Classify Statistical Outliers

**Classify Statistical Outlier** is another type of noise classifier. This routine first creates a surface from a neighborhood of points (**Neighbor Count**). Then, residuals in respect to this surface are computed for each point. Any points with residuals that fall outside of the sigma specified (**Sigma Multiplier**) are assigned to the **Output Class**.

In the example below, any points with a residual larger than sigma 1.0 are assigned to the Low Point (noise) class:

<figure><img src="/files/XFtDT3ATKARSEK3GiJZN" alt=""><figcaption></figcaption></figure>

This tool is ideal for cleaning high density data sets and for tightening up planar surfaces. In general, the **Sigma Multiplier** parameter controls how aggressively this tool classifies points as noise. Below are some example values:

| Sigma Multiplier Value | Use-case                                                                                                                         |
| ---------------------- | -------------------------------------------------------------------------------------------------------------------------------- |
| 3.00                   | Will classify very few points as noise. Ideal for high-precision point clouds.                                                   |
| 1.00                   | Will classify a moderate amount of points as noise, and generally a good starting point when experimenting with outlier removal. |
| 0.5                    | Will classify about half of the data set as noise, and useful when attempting to drastically improve low precision data sets.    |


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