80 lines
2.2 KiB
ReStructuredText
80 lines
2.2 KiB
ReStructuredText
.. _grid_search:
|
|
|
|
===========
|
|
Grid Search
|
|
===========
|
|
|
|
.. currentmodule:: sklearn.grid_search
|
|
|
|
|
|
Grid Search is used to optimize the parameters of a model
|
|
(e.g. Support Vector Classifier, Lasso, etc.) using cross-validation.
|
|
|
|
Main class is :class:`GridSearchCV`.
|
|
|
|
Examples
|
|
========
|
|
|
|
See :ref:`example_grid_search_digits.py` for an example of
|
|
Grid Search computation on the digits dataset.
|
|
|
|
See :ref:`example_grid_search_text_feature_extraction.py` for an example
|
|
of Grid Search coupling parameters from a text documents feature extractor
|
|
(n-gram count vectorizer and TF-IDF transformer) with a classifier
|
|
(here a linear SVM trained with SGD with either elastic net or L2 penalty).
|
|
|
|
.. note::
|
|
|
|
Computations can be run in parallel if your OS supports it, by using
|
|
the keyword n_jobs=-1, see function signature for more details.
|
|
|
|
|
|
Alternatives to brute force grid search
|
|
=======================================
|
|
|
|
Model specific cross-validation
|
|
-------------------------------
|
|
|
|
.. currentmodule:: sklearn
|
|
|
|
Some models can fit data for a range of value of some parameter almost
|
|
as efficiently as fitting the estimator for a single value of the
|
|
parameter. This feature can be leveraged to perform a more efficient
|
|
cross-validation used for model selection of this parameter.
|
|
|
|
The most common parameter amenable to this strategy is the parameter
|
|
encoding the strength of the regularizer. In this case we say that we
|
|
compute the **regularization path** of the estimator.
|
|
|
|
Here is the list of such models:
|
|
|
|
.. autosummary::
|
|
:toctree: generated/
|
|
:template: class.rst
|
|
|
|
linear_model.RidgeCV
|
|
linear_model.RidgeClassifierCV
|
|
linear_model.LarsCV
|
|
linear_model.LassoLarsCV
|
|
linear_model.LassoCV
|
|
linear_model.ElasticNetCV
|
|
|
|
|
|
Information Criterion
|
|
---------------------
|
|
|
|
Some models can offer an information-theoretic closed-form formula of the
|
|
optimal estimate of the regularization parameter by computing a single
|
|
regularization path (instead of several when using cross-validation).
|
|
|
|
Here is the list of models benefitting from the Aikike Information
|
|
Criterion (AIC) or the Bayesian Information Criterion (BIC) for automated
|
|
model selection:
|
|
|
|
.. autosummary::
|
|
:toctree: generated/
|
|
:template: class.rst
|
|
|
|
linear_model.LassoLarsIC
|
|
|