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?
: 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.
: 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).
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?
: 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.
: 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).
