: Run the DFS algorithm to output a new "feature matrix" containing these high-level, multi-layered insights. Applications for "What We Leave Behind"
: A deep feature could aggregate the frequency and variety of digital interactions over time to measure the "weight" of a person's digital remains. What We Leave Behind
: 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
To build a deep feature using a tool like Featuretools, follow this workflow: What We Leave Behind
: By applying mathematical functions to time-series data, you can create features that predict how quickly certain "left behind" artifacts lose relevance or visibility.