# Viewing the Feature Importance Information in Beam Beam's Feature Importance module uses advanced machine learning models to identify the event features that have the greatest impact on your demand. This is done automatically after you create an analysis and upload demand data for an individual location. The results are a list of features with a significance rating for each. The feature importance results are for an individual analysis of the location you selected for Beam. For example, restaurants tend to be highly impacted by sports and public holidays while hotels tend to be more impacted by conferences and concerts. By running feature importance you can see how your business locations are impacted by events. Feature Importance covers the attendance and non-attended categories shown plus severe weather in the unscheduled category. It does not cover other categories not shown (such as disaster events). Below is an example:

Category Importance example in a Beam Analysis

In this example, we can see a very high impact from conferences, expos, performing arts, observances, and academic events. The other features have less impact on this location and as shown as "Minor" or "Marginal" impact. If a feature shows a "-" then it does not have a statistically significant impact on demand based on the data uploaded or there are no events from this feature taking place within the selected radius around the location. You can use Beam to find out how different types of events are impacting your demand. If you are not sure what types of events impact your demand then create a Beam analysis with your demand data and look at the results. Many customers use the Beam results to help decide what event-based machine learning features to add to their machine learning models. Beam uses the [Get Feature Importance](https://app.gitbook.com/s/kEFs8urDbSJqBmXUI3Lv/beam/analyses/get-feature-importance "mention") API call to return sets of features related to important features based on the feature importance results for analysis. Anything that you can do in the WebApp can also be done via the [Beam API](https://app.gitbook.com/s/kEFs8urDbSJqBmXUI3Lv/beam). Use the Beam API if you want to create a large number of analyses. We also provide a "View ML Features" option for an analysis - see [Find the ML Features to use in your forecast](https://docs.predicthq.com/webapp-support/beam-relevancy-engine/feature-importance-with-beam-find-the-ml-features-to-use-in-your-forecasts) for more info on how to use this. ### *p*-values *p*-values represent statistical significance and we assign a readable label to each feature, based on its *p*-value. There is a toggle to view as *p*-values for advanced users. This is intended for use by data science teams interested in digging deeper into the results.

Note: very small *p*-values will be shown as <0.001. This can refer to results that are smaller than 0.001, e.g. 0.0005. A category that is considered to have no impact on the location will show a "-" label and no *p*-value will be shown. The mapping of *p*-values to the impact levels shown on the Category Importance graphs is below:\\

* 0.6 ≤ *p* < 1.0 - Marginal * 0.1 ≤ *p* < 0.6 - Minor * 0.075 ≤ *p* < 0.1 - Moderate * 0.05 ≤ *p* < 0.075 - High * *p* < 0.05 - Very high --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.predicthq.com/webapp-support/beam-relevancy-engine/viewing-the-category-importance-information-in-beam.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.