OptiML is an optimization process for model selection and parametrization that automatically finds the best supervised model to help you solve classification and regression problems.

Using Bayesian Parameter Optimization, OptiML creates and evaluates hundreds of supervised models (decision trees, ensembles, logistic regressions, and deepnets) and returns a list of the best models for your data. Eliminating the need for manual, trial-and-error based exploration of algorithms and parameters, OptiML saves significant time and provides improved performance for Machine Learning practitioners of all levels.