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()
lr.fit(X, y)
Algorithms
----------
.. currentmodule:: dask_glm.algorithms
.. autosummary::
admm
gradient_descent
lbfgs
newton
proximal_grad
Regularizers
------------
.. currentmodule:: dask_glm.regularizers
.. autosummary::
ElasticNet
L1
L2
Regularizer