Generalized Linear Models ========================= .. currentmodule:: dask_ml.linear_model .. autosummary:: LinearRegression LogisticRegression PoissonRegression Generalized linear models are a broad class of commonly used models. These implementations scale well out to large datasets either on a single machine or distributed cluster. They can be powered by a variety of optimization algorithms and use a variety of regularizers. These follow the scikit-learn estimator API, and so can be dropped into existing routines like grid search and pipelines, but are implemented externally with new, scalable algorithms and so can consume distributed dask arrays and dataframes rather than just single-machine NumPy and Pandas arrays and dataframes. Example ------- .. ipython:: python from dask_ml.linear_model import LogisticRegression from dask_ml.datasets import make_classification X, y = make_classification(chunks=50) lr = LogisticRegression(), y) Algorithms ---------- .. currentmodule:: dask_glm.algorithms .. autosummary:: admm gradient_descent lbfgs newton proximal_grad Regularizers ------------ .. currentmodule:: dask_glm.regularizers .. autosummary:: ElasticNet L1 L2 Regularizer