What We Leave Behind Apr 2026

: By applying mathematical functions to time-series data, you can create features that predict how quickly certain "left behind" artifacts lose relevance or visibility.

: Run the DFS algorithm to output a new "feature matrix" containing these high-level, multi-layered insights. Applications for "What We Leave Behind" What We Leave 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? : By applying mathematical functions to time-series data,

: 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). A depth of 1 might calculate "average session

: 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".

If your project is a on human legacy, deep features can quantify abstract concepts: