What We Leave Behind Access

: 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.

: 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

: Run the DFS algorithm to output a new "feature matrix" containing these high-level, multi-layered insights. Applications for "What We Leave Behind" : Using Deep Feature Factorization (DFF) , you

: A deep feature could aggregate the frequency and variety of digital interactions over time to measure the "weight" of a person's digital remains. : Run the DFS algorithm to output a

To build a deep feature using a tool like Featuretools, follow this workflow:

: 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).