: Choose Aggregation primitives (calculating values across many related records, such as MEAN amount of data left behind) or Transform primitives (performing operations on a single table, such as YEAR from a timestamp).
: Run the DFS algorithm to output a new "feature matrix" containing these high-level, multi-layered insights. Applications for "What We Leave Behind"
: Specify the max_depth . A depth of 1 might calculate "average session time," while a depth of 2 could calculate the "average of the maximum session times across all devices". What We Leave Behind
: Using Deep Feature Factorization (DFF) , you can localize similar themes across a collection of images or memories to find common threads in what is left behind.
If you'd like to dive into the technical setup, would you prefer to see using Featuretools or a conceptual breakdown of which data points would make the best features for your specific dataset? A depth of 1 might calculate "average session
: By applying mathematical functions to time-series data, you can create features that predict how quickly certain "left behind" artifacts lose relevance or visibility.
: Identify your "base" table (e.g., Users ) and related tables (e.g., Digital Footprint , Physical Artifacts ). : By applying mathematical functions to time-series data,
To build a deep feature using a tool like Featuretools, follow this workflow: