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. This room labeling example demonstrates a simple Multiclass Classifier use case, where the ML model is used to label new spaces drawn in Rhino.
Depending on the dataset and the algorithm’s calibration, the accuracy of predicted labels may vary and it is important to oversee the process and review the results. Manual adjustments or additional calibration may be necessary.
Download the latest version of LunchBox to give it a try! Here are a few related articles that can help you get started with LunchBox ML: