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 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
If your project is a on human legacy, deep features can quantify abstract concepts: If you'd like to dive into the technical
: By applying mathematical functions to time-series data, you can create features that predict how quickly certain "left behind" artifacts lose relevance or visibility. : Using Deep Feature Factorization (DFF) , you
: 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.
: Run the DFS algorithm to output a new "feature matrix" containing these high-level, multi-layered insights. Applications for "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).