The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist 😎. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameters tuning to find the best model 🏆. It is no black-box as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model).
Golden Features are new features constructed from original data which have great predictive power. Please see the Golden Features section in the documentation for more details about how are they constructed.
The Golden Features are constructed only once during AutoML fit. They are saved in results_path in golden_features.json file.
After creation of the Golden Features they are added to the original data and following algorithms are trained: