Page tree

Early Access: This documentation applies to the Early Access releases of the Alteryx Analytics Cloud and some hosted applications. Other hosted applications may be All Access at this time. If you are interested in getting access, please contact your Alteryx representative.



During the Feature Engineering stage, use primitives to create new, engineered features. Engineered features help to better represent the underlying problem in your model. You can also review helpful information about the correlations of your new features.  

Features Versus Columns

In Machine Learning®, we refer to the columns of your dataset as features. Features are measurable values or characteristics of your data.


Check the box next to each primitive you want to include in Feature Engineering. Then select Save Changes to automatically calculate new features. Note, that a selected primitive applies to all the columns that have a matching data type.

Don't initially turn on all primitives. A large number of features might make it difficult for the model to find patterns in your data. We recommend that you read through each primitive description. Give some thought to the primitives that might be useful for your data.


The Features panel shows the features that we calculated from your select primitives. We mark engineered features with an asterisk (*). The Primitive Creator column displays the primitive we used to create each feature.

Select Show Origin Features to include the original features of your dataset in the list.

Engineered Correlations

The Correlation Matrix shows the strength of correlations between your original and engineered features. Each cell in the Correlation Matrix shows the relationship between pairs of features.

This page has no comments.