These include binning, encoding categorical features ... By intelligently selecting and transforming these features, a machine learning model can be made more accurate and reliable, capable ...

Businesspeople need to demand more from machine learning so they can connect data scientists’ work to relevant action. This requires basic machine learning literacy — what kinds of problems ...

Machine learning may be eating software, but it looks as though feature stores may be eating machine learning. In the rush to develop and roll machine learning applications into production, ...

The high demand for machine learning has produced a large ... Another approach is to turn a categorical feature into a set of variables using one-hot encoding. In the above example, turning ...

Their work demonstrates that quantum circuits for data encoding in quantum machine learning can be greatly simplified without compromising accuracy or robustness. The research was published Sept.

Stage 2 is online prediction with batch features. Features in machine learning are individual measurable properties or characteristics of a phenomenon used to build a model. Batch features are ...

Yesterday, RSA acquired Fortscale, a UEBA veteran. RSA plans to make Fortscale a machine learning analytics feature set for its NetWitness platform. (It should also be noted that even as an ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...