Get Feature Importance
Get relevant ML features based on a Beam Analysis.
This endpoint provides the relevant ML features (based on feature importance testing) that are shown to impact the demand for the Beam Analysis. These are the features you should take into your forecasting model to improve your model accuracy.
These values represent each group of features' statistical significance when it comes to impacting observable incremental/decremental changes in demand.
The easiest way to get these ML features from our Features API to be used in your models is by using the Beam analysis_id
in your Features API request.
Request
HTTP Request
Path Parameters
analysis_id
An existing Beam Analysis ID.
Response
Response Fields
feature_importance
array
List of Feature Importance groups. Please refer to the Feature Importance Response Fields section below for the structure of each record.
Feature Importance Response Fields
feature_group
string
The name of the group. This typically aligns to an event category.
E.g. severe-weather
, concerts
features
array
The names of the features in the feature group. These refer directly to features available in Features API.
E.g.
p_value
float
The p-value associated with this feature group for this analysis. It indicates how important the features in the group are in terms of demand.
The lower the p-value, the more important the feature group is.
E.g. 0.312
important
boolean
A true
of false
value indicating whether the feature group is considered important for this analysis.
Equivalent to p_value < 0.1
We suggest using this value to determine whether or not to include this group of features in your modeling.
Examples
Guides
Below are some guides relevant to this API:
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