6.12.1. scikits.learn.grid_search.GridSearchCV

class scikits.learn.grid_search.GridSearchCV(estimator, param_grid, loss_func=None, score_func=None, fit_params={}, n_jobs=1, iid=True)

Grid search on the parameters of a classifier

Important members are fit, predict.

GridSearchCV implements a “fit” method and a “predict” method like any classifier except that the parameters of the classifier used to predict is optimized by cross-validation

Parameters :

estimator: object type that implements the “fit” and “predict” methods :

A object of that type is instanciated for each grid point

param_grid: dict :

a dictionary of parameters that are used the generate the grid

loss_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediciton (small is good) if None is passed, the score of the estimator is maximized

score_func: callable, optional :

function that takes 2 arguments and compares them in order to evaluate the performance of prediciton (big is good) if None is passed, the score of the estimator is maximized

fit_params : dict, optional

parameters to pass to the fit method

n_jobs: int, optional :

number of jobs to run in parallel (default 1)

iid: boolean, optional :

If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.

Notes

The parameters selected are those that maximize the score of the left out data, unless an explicit score_func is passed in which case it is used instead. If a loss function loss_func is passed, it overrides the score functions and is minimized.

Examples

>>> from scikits.learn import svm, grid_search, datasets
>>> iris = datasets.load_iris()
>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
>>> svr = svm.SVR()
>>> clf = grid_search.GridSearchCV(svr, parameters)
>>> clf.fit(iris.data, iris.target) 
GridSearchCV(n_jobs=1, fit_params={}, loss_func=None, iid=True,
       estimator=SVR(kernel='rbf', C=1.0, probability=False, ...
       ...

Methods

fit(X[, y, refit, cv])	Run fit with all sets of parameters
score(X[, y])

__init__(estimator, param_grid, loss_func=None, score_func=None, fit_params={}, n_jobs=1, iid=True)

fit(X, y=None, refit=True, cv=None, **kw)

Run fit with all sets of parameters

Returns the best classifier

Parameters :

X: array, [n_samples, n_features] :

Training vector, where n_samples in the number of samples and n_features is the number of features.

y: array, [n_samples] or None :

Target vector relative to X, None for unsupervised problems

cv : crossvalidation generator

see scikits.learn.cross_val module

refit: boolean :

refit the best estimator with the entire dataset