skorch

fit()` and `predict()`), relying on the learned attributes (object properties with a single underscore suffix, e.g. `coef_`). The issue is that when a third-party estimator does not expose these attributes, truly gnarly exceptions and tracebacks occur. Yellowbrick is meant to aid machine learning diagnostics reasoning, therefore instead of just allowing drop-in functionality that may cause confusion, we’ve created a wrapper functionality that is a bit kinder with it’s messaging. Looks like there are Yellowbrick wrappers for XGBoost, CatBoost, CuML, and Spark MLib; but not for NNs yet. https://www.scikit-yb.org/en/latest/api/contrib/wrapper.html... From the RAPIDS.ai CuML team: https://docs.rapids.ai/api/cuml/stable/ : > cuML is a suite of fast, GPU-accelerated machine learning algorithms designed for data science and analytical tasks. Our API mirrors Sklearn’s, and we provide practitioners with the easy fit-predict-transform paradigm without ever having to program on a GPU. > As data gets larger, algorithms running on a CPU becomes slow and cumbersome. RAPIDS provides users a streamlined approach where data is intially loaded in the GPU, and compute tasks can be performed on it directly. CuML is not an NN library; but there are likely performance optimizations from CuDF and CuML that would accelerate performance of NNs as well. Dask ML works with models with sklearn interfaces, XGBoost, LightGBM, PyTorch, and TensorFlow: https://ml.dask.org/ : > Scikit-Learn API > In all cases Dask-ML endeavors to provide a single unified interface around the familiar NumPy, Pandas, and Scikit-Learn APIs. Users familiar with Scikit-Learn should feel at home with Dask-ML. dask-labextension for JupyterLab helps to visualize Dask ML CFGs which call predictors and classifiers with sklearn interfaces: https://github.com/dask/dask-labextension