We have developed a high-level user-oriented machine-learning library julearn in Python (>=3.6). This library provides users with the possibility of testing ML models directly from pandas dataframes, while keeping the flexibiliy of using scikit-learn’s models. You can create pipelines with preprocessing steps. In addition, we also provide the functionality to...
Interpretability is an important consideration for machine learning systems, especially in the clinical domain. We took first steps to develop a methods that creates complex representations of base features while still keeping them interpretable.