Weekly Workflow: Use LunchBox ML to Predict Solar Radiation

LunchBox includes Machine Learning components (LunchBoxML) that make Accord.NET and ML.NET workflows accessible within Grasshopper. These components allow users to train, save, and test machine learning models using a variety of algorithms. This example uses LunchBox’s Regression Trainer alongside Ladybug’s environmental modeling tools to predict incident solar radiation for an irregular surface.

Weekly Workflow: Train an Algorithm to Label Spaces for You

Labeling spaces can be a tedious process, but Machine Learning tools have the potential to give designers reprieve from manually typing in each space name. Classifier algorithms are able to reference training data and group lists of new elements into classes based on their characteristics and similarities to the data on which it was trained.