Process Models - Regressions! — Changelog

Expanded Model Selection: We've added two more models to our repertoire: Elastic Net Linear Regression: Combines Ridge and Lasso regularization for a balanced, interpretable model. Gaussian Process Regressor (GPR): Ideal for understanding uncertainty in predictions, treating each input variable with consideration to their covariance.
New Scaling Options: MinMax and Standard scalers are provided for additional data preprocessing.
Interactive Visualization: Explore the Partial Dependence and Braid plots to better understand the outputs of the model.
Prediction for Experimental Design: Dive into the relationships between process parameters and key performance indicators using your own custom ML prediction model and determine what design space to study next.