Evolutionary ML

The rgOne® platform’s Automated Machine Learning (AML) model training is driven by evolutionary algorithms that zero in on the best model for a precise outcome — across a wide range of possible setups.

Key Benefits

Leverage evolutionary modeling tactics to train, optimize, and automatically update models tuned to specific business objectives such as acquisition, cross-sell or retention. Optimize your results with modeling capabilities to alter model type and parameters, fitness functions to assess models, and an efficient search mechanism to select the best model. 

Real ROI against real metrics.

Drive evolution based on fitness — on how well a model predicts or maps customer behaviors and outcomes. Based on real-world, measurable goals.

Evolve automatically.

Search quickly and efficiently across a range of variants – that is, model types and parameters – with automated assessments of fitness through multiple generations.

Measure & retrain instantly.

Use measurements from live customer interactions to retrain models, starting with existing solutions — or from scratch — to refresh decisions and optimize every interaction.

Key Highlights

Outcomes clearly defined, not delayed.

You launch evolutionary machine learning by starting with a defined business goal, such as increasing customer retention by a factor of X. This ignites the model building engine by continuously measuring each variant against that specific goal.

No need for Data Scientists to run repeated, laborious trials or manually vary model parameters to choose the “best” model: rgOne auto-selects from a wide range of models and variants, and chooses the best goal-specific possibilities. In every generation.

Solution fitness, not frustration.

Think of it as survival of the fittest: An evolutionary algorithm evaluates each variant against a specific goal, auto-selects the winners, and creates new variants for the next generation.

This evolution drives toward “the fittest” solution — the one that most accurately meets the business objective across the training data. And training stops upon reaching optimal fitness, or after a specific number of generations.

  • Solutions evolve over generations, and can be compared and used as they improve
  • Easily measure the growing accuracy — and ability to meet target metrics — at any time during training
  • The proven approach builds effective business solutions for even the most complex, matrixed and exacting industries

Related Solutions

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