What We Leave Behind < COMPLETE - 2024 >

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

In machine learning, developing a for a project like "What We Leave Behind" involves using Deep Feature Synthesis (DFS) to automatically generate complex features from relational data. This process moves beyond simple raw data by stacking mathematical "primitives" (like sum, mean, or count) across related tables to reveal hidden patterns. Core Development Steps 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" : A deep feature could aggregate the frequency

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