Prime Optima

Machine learning algorithms are widely used in various applications and areas. To fit a machine learning model to different problems, its parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. A common challenge when it comes to most optimisation software packages is that they are specialised and extremely sensitive to the parameters used to configure them, also it often require deep knowledge of machine learning algorithms and appropriate hyper-parameter optimization techniques. Although several automatic optimization techniques exist, they have different strengths and drawbacks when applied to different types of problems.

Ideally you would want an easy to use software library that can automatically search the parameter space for the best combination for your application.

Prime Optima is a hyper-optimisation engine capable of optimising the parameters of regular solvers using Bayesian statistics. A solution for industrial users, data analysts, and researchers who have a need for meta optimisation of existing solutions for prospective analysis and planning to get concrete and actionable feedback of your optimising models that can improve the performance of your processes.

Prime Optima

autor Tenfifty AB