---
schema_version: "1"
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title: Outliers Alert
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_ai_ec_quality_alert_outliers.md
canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_ai_ec_quality_alert_outliers.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: db2ef128cde516efcee1ba0ea603db351f1e32a7bc87f48583fa47e1cb80428e
description: Outliers are data points that significantly differ from the majority in a dataset. The alert indicates that at least one outlier was detected and that there's a presence of uncommonly small or large numbers. Outliers can skew model performance, reduce accuracy, and lead to incorrect forecasts.
products:
  - Data 360
release: "264"
---
# Outliers Alert

Outliers are data points that significantly differ from the majority in a dataset. The alert indicates that at least one outlier was detected and that there's a presence of uncommonly small or large numbers. Outliers can skew model performance, reduce accuracy, and lead to incorrect forecasts.

## Actions to Consider

There are two types of outliers.

- incorrect values due to data entry errors, processing errors, or other issues.
- correct values that reflect an extraordinary, non-recurring, or infrequent event.

Investigate to determine which outliers to keep and which ones to exclude from your model. Exclude incorrect values and consider fixing them before model training.

## Detection Methodology

For a variable, Model Builder:

- calculates the global mean and global standard deviation of its values.
- designates an outlier as any value that's greater than, or less than five standard deviations away from the global mean.

## Example

A retailer wants to predict unit sales for different products. To achieve this, the retailer builds a regression model with these input variables.

- product ID
- unit price
- day of the week
- store location
- stock availability
- competitor unit price
- promo event

After model training, an alert displays because a high outlier score is detected for a product. Most products sell between 10 and 100 units per day but records show sales over 1000 units per day. To resolve the issue, here are some actions to consider.

- Remove data points that may have resulted from errors.
- Include the outliers score during training to help the model learn patterns that indicate regular sales amounts. Then, use the data to identify transactions that significantly deviate from the norm.
- Retrain the model with an updated dataset.